dLp10CFIvxI.json

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        "text": " Hello, you are here watching another coding challenge",
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        "text": " and this coding challenge, maybe this should just fit",
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        "text": " and be in one of my tutorial videos,",
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        "text": " but I'm gonna make it a coding challenge",
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        "text": " because I'm gonna attempt to do it in one video.",
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        "text": " And what I'm doing is recreating something",
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        "text": " that I've done before in some",
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        "text": " of my machine learning tutorials.",
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        "text": " And it was suggested here,",
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        "text": " I don't know if it was suggested exactly,",
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        "text": " but a Twitter user, Calstube Old Podcar,",
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        "text": " apologies if I'm pronouncing the name incorrectly,",
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        "text": " created this interactive simulation",
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        "text": " of linear regression using TensorFlow.js.",
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        "text": " And so this is very similar",
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        "text": " to something that I've done previously, right?",
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        "text": " I have this video, linear regression with gradient descent,",
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        "text": " where I just did this with plain JavaScript.",
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        "text": " And then you could also look at this other video,",
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        "text": " which I go through the mathematics",
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        "text": " of gradient descent a little bit.",
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        "text": " But here's the thing, going through the mathematics,",
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        "text": " making this video where I implement the mathematics",
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        "text": " and perhaps background for this video,",
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        "text": " one of the exciting things about doing this",
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        "text": " with TensorFlow.js is TensorFlow.js has a nice API",
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        "text": " for optimizing loss functions",
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        "text": " with the gradient descent algorithm built into it.",
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        "text": " So I could just do things.",
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        "text": " So let's make a, I gotta come back here,",
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        "text": " but let's make a list.",
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        "text": " All right, so first of all,",
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        "text": " what is linear regression anyway?",
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        "text": " So let's say we have a space,",
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        "text": " and I drew this as like a canvas,",
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        "text": " but really I should be talking just about a generic",
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        "text": " kind of two-dimensional Cartesian plane.",
        "tokens": [
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        "text": " In that plane, there are a bunch of points.",
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        "text": " The idea of linear regression is to figure out,",
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        "text": " can we fit, oh, this is a time for another colored marker.",
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        "text": " Can we fit a line into this two-dimensional space",
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        "text": " that approximates all of these points as best we can?",
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        "text": " And I can visually just kind of make myself",
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        "text": " do this like this.",
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        "text": " So I can eyeball it and say, this line kind of gets close.",
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        "text": " What we're trying to do is minimize,",
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        "text": " I've never, all of these,",
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        "text": " this is the most beautiful diagram I've ever made,",
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        "text": " all of these distances of all of the points to the line.",
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        "text": " The idea here then is that we can make some predictions.",
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        "text": " If this data, if this x-axis represents height,",
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        "text": " we might predict on the y-axis weight.",
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        "text": " You can think of kinds of data sets,",
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        "text": " simple 2D data sets where there's a linear relationship",
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        "text": " between the one field of data and another field of data.",
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        "text": " So if we pick a new height, we can kind of make a guess",
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        "text": " approximately what that weight is gonna be.",
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        "text": " That's the idea of linear regression.",
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        "text": " It's incredibly simple.",
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        "text": " A lot of data isn't two-dimensional.",
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        "text": " A lot of data doesn't fit a line.",
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        "text": " You know, maybe a curve fits it better.",
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        "text": " And this is more complex scenarios will come",
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        "text": " as we move forward and make more scenarios",
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        "text": " with complex polynomial equations",
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        "text": " or neural network-based learning",
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        "text": " and other types of machine learning algorithms.",
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        "temperature": 0,
        "text": " But this is a good place for us to start.",
        "tokens": [
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        "text": " So what do we need?",
        "tokens": [
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      },
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        "avg_logprob": -0.18878607316450638,
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        "text": " We need a data set.",
        "tokens": [
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        "temperature": 0,
        "text": " So we need a set of x's and y's.",
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        "avg_logprob": -0.18878607316450638,
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        "text": " This is the data set.",
        "tokens": [
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      },
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        "text": " Right, we need x's and y's.",
        "tokens": [
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        "no_speech_prob": 0.000017778538676793687,
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        "text": " And I'm gonna create that data set",
        "tokens": [
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      {
        "avg_logprob": -0.256456979891149,
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        "text": " through interactive clicking.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.256456979891149,
        "compression_ratio": 1.8612244897959183,
        "end": 211.9,
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        "text": " Interactive clicking is the way I'm gonna create",
        "tokens": [
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      {
        "avg_logprob": -0.256456979891149,
        "compression_ratio": 1.8612244897959183,
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        "text": " that data set with the mouse.",
        "tokens": [
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      },
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        "text": " I need to have something called a loss function.",
        "tokens": [
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        "text": " The loss function is a way of computing the error.",
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        "text": " And there are a bunch of different loss functions,",
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        "end": 228.44,
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        "text": " and we'll see these as I use TensorFlow.js",
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        "text": " in more tutorials.",
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        "text": " I can select different kinds of loss functions",
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        "text": " for different scenarios.",
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        "text": " But in this scenario, I'm gonna use a simple basic one,",
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        "avg_logprob": -0.20999808609485626,
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        "text": " which I believe is called root mean squared error.",
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        "text": " Did I say that correctly?",
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        "text": " Is that the right name of it?",
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        "text": " But the idea is that I wanna look at all of those distances.",
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        "text": " Okay, I'm back,",
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      },
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        "text": " because I started talking about the loss function,",
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        "text": " and I realized I really didn't draw.",
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        "text": " I'm not actually looking for the distance",
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        "text": " from the point to that line, which would be perpendicular.",
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        "text": " I'm looking for this vertical distance,",
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        "temperature": 0,
        "text": " which is, so this is what I'm trying to minimize.",
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        "text": " Right, I'm trying to minimize and get a line that has,",
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        "text": " and this, that is the least,",
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        "text": " the sum of all these distances is the smallest number,",
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        "text": " minimizing the loss.",
        "tokens": [
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        "compression_ratio": 1.732394366197183,
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        "no_speech_prob": 0.000001760342343004595,
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        "temperature": 0,
        "text": " So I have a loss function, I need that.",
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        "text": " I also need, in TensorFlow.js,",
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        "text": " something called an optimizer.",
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        "temperature": 0,
        "text": " And the optimizer is the thing that allows me to minimize",
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        "seek": 29234,
        "start": 292.5,
        "temperature": 0,
        "text": " the loss function.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
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        "seek": 29234,
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        "temperature": 0,
        "text": " And in order to do that,",
        "tokens": [
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      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 301.53999999999996,
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        "no_speech_prob": 0.00001045153294398915,
        "seek": 29234,
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        "temperature": 0,
        "text": " I also need to have a learning rate.",
        "tokens": [
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      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 302.38,
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        "temperature": 0,
        "text": " So these are all,",
        "tokens": [
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      {
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        "end": 304.17999999999995,
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        "text": " I actually missed something very important here,",
        "tokens": [
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      {
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        "end": 305.29999999999995,
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        "start": 304.17999999999995,
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        "text": " but these are all the pieces.",
        "tokens": [
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      {
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        "compression_ratio": 1.9437229437229437,
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        "text": " I need the data, I need to define a loss function,",
        "tokens": [
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      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 310.34,
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        "temperature": 0,
        "text": " I need to define an optimizer.",
        "tokens": [
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      {
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        "compression_ratio": 1.9437229437229437,
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        "text": " I say, hey, optimizer,",
        "tokens": [
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      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 314.44,
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        "no_speech_prob": 0.00001045153294398915,
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        "start": 311.53999999999996,
        "temperature": 0,
        "text": " minimize the loss function with this learning rate.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 317.29999999999995,
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        "seek": 29234,
        "start": 314.44,
        "temperature": 0,
        "text": " So keep tweaking the parameters, tweaking the parameters.",
        "tokens": [
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          11,
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      },
      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 318.38,
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        "seek": 29234,
        "start": 317.29999999999995,
        "temperature": 0,
        "text": " So that's the thing I forgot.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.17716099105718483,
        "compression_ratio": 1.9437229437229437,
        "end": 319.53999999999996,
        "id": 117,
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        "seek": 29234,
        "start": 318.38,
        "temperature": 0,
        "text": " What are those parameters?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
        "end": 324.54,
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        "seek": 31954,
        "start": 319.54,
        "temperature": 0,
        "text": " Well, the formula for a line is y equals mx plus b.",
        "tokens": [
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        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
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        "text": " M is often referred to as the slope, b as the,",
        "tokens": [
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      {
        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
        "end": 332.06,
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        "temperature": 0,
        "text": " I'm back, because I looked it up.",
        "tokens": [
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        "avg_logprob": -0.2506144010103666,
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        "end": 337.06,
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        "seek": 31954,
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        "temperature": 0,
        "text": " M is the slope, and b referred to as the y-intercept.",
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        "text": " Kind of like bias, by the way,",
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        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
        "end": 342.66,
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        "text": " if you've watched some of my other neural network tutorials,",
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        "avg_logprob": -0.2506144010103666,
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        "end": 344.54,
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        "text": " because this is like the thing we're doing",
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      {
        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
        "end": 345.38,
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        "text": " with all the neurons.",
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      },
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        "avg_logprob": -0.2506144010103666,
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        "text": " Oh, it's all so connected,",
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        "avg_logprob": -0.2506144010103666,
        "compression_ratio": 1.6626984126984128,
        "end": 349.04,
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        "text": " but we're just living in this very simple place.",
        "tokens": [
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        "text": " So I need these parameters, I need these variables,",
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        "text": " because that's what's going to allow me",
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        "text": " to create the predictions that are on the line",
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        "text": " to compare with the actual points,",
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        "text": " and compute the loss, minimize it, tweaking these values.",
        "tokens": [
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        "avg_logprob": -0.2019572294959726,
        "compression_ratio": 1.8416988416988418,
        "end": 368.24,
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        "temperature": 0,
        "text": " So tweak these values, minimizing the loss.",
        "tokens": [
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        "text": " This is what we're doing.",
        "tokens": [
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        "text": " And I've done this before in great detail.",
        "tokens": [
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        "text": " This is gonna be in less detail,",
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        "text": " because TensorFlow.js is gonna do a lot of this for us.",
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        "text": " The thing that's a little extra complicated",
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        "compression_ratio": 1.6962025316455696,
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        "text": " is we can't just work with arrays of numbers and variables",
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      {
        "avg_logprob": -0.20164997962213332,
        "compression_ratio": 1.6962025316455696,
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        "text": " in the way that we're used to in JavaScript.",
        "tokens": [
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      {
        "avg_logprob": -0.20164997962213332,
        "compression_ratio": 1.6962025316455696,
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        "temperature": 0,
        "text": " And so this is what brings me to,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20164997962213332,
        "compression_ratio": 1.6962025316455696,
        "end": 389.64,
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        "no_speech_prob": 0.000005771911219198955,
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        "text": " if you haven't looked at these particular videos",
        "tokens": [
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      {
        "avg_logprob": -0.20164997962213332,
        "compression_ratio": 1.6962025316455696,
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        "text": " that I've made already, what's a TensorFlow tensor?",
        "tokens": [
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        "avg_logprob": -0.20164997962213332,
        "compression_ratio": 1.6962025316455696,
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        "no_speech_prob": 0.000005771911219198955,
        "seek": 37888,
        "start": 393.8,
        "temperature": 0,
        "text": " What's a variable?",
        "tokens": [
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      },
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        "text": " What's an operation?",
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        "start": 395.54,
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        "text": " How's the memory management stuff?",
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        "text": " This is stuff we're gonna have to lean on",
        "tokens": [
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        "text": " while I build this example.",
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        "text": " And this should be, by the way,",
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        "text": " an actual practical example of where I need a tf variable.",
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        "text": " So I kind of, in this video,",
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        "text": " explained what a tf variable is,",
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        "text": " and then people just kind of moved on",
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        "text": " and didn't use it for anything.",
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        "text": " So hopefully this will show us that.",
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        "text": " All right, how about we write some code now?",
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        "text": " So actually, I magically appeared over here for a second,",
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        "text": " because instead of, I did make a little mistake here.",
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        "text": " I mean, root mean squared",
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        "text": " is a perfectly legitimate loss function,",
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        "text": " but most linear regression with gradient descent examples",
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        "text": " will not bother with the root.",
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        "text": " And the root refers to square root.",
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        "text": " We just want the mean squared error,",
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        "text": " which means, if I say that this value is y,",
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        "text": " and this value is the guess,",
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        "text": " the error is guess minus y and squared.",
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        "text": " And if I do that for all of these, that's the mean.",
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        "text": " And average them, it's the mean.",
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        "text": " So I can really sum them or mean them.",
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        "text": " Well, it's gonna take care of that for us.",
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        "text": " So we're just gonna use the mean squared error.",
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        "text": " But that's the idea.",
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        "text": " We take the differences.",
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        "text": " The reason why we have to square it,",
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        "text": " well, for a variety of reasons.",
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        "text": " One, it has to do with the derivative stuff",
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        "text": " that's in my other videos,",
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        "temperature": 0,
        "text": " but also just because positive or negative,",
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        "text": " it's the distance, the size of the error,",
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        "text": " whether it's up or down, which is key.",
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        "text": " All right, so here's the amount of code",
        "tokens": [
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        "temperature": 0,
        "text": " we're gonna start with.",
        "tokens": [
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      },
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        "seek": 46652,
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        "temperature": 0,
        "text": " I'm using p5 so that I can draw stuff.",
        "tokens": [
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        "temperature": 0,
        "text": " I'm making a canvas, and the background is zero,",
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        "text": " which means it's black, and this is what I have so far.",
        "tokens": [
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        "text": " So let's look at our list over here,",
        "tokens": [
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        "seek": 49336,
        "start": 493.40000000000003,
        "temperature": 0,
        "text": " and let's first add the data set, the Xs and Ys.",
        "tokens": [
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        "text": " So this is gonna be easy,",
        "tokens": [
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        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 503.88,
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        "no_speech_prob": 0.0000189251295523718,
        "seek": 49336,
        "start": 498.88,
        "temperature": 0,
        "text": " because I just wanna have Xs be an array,",
        "tokens": [
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        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
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        "no_speech_prob": 0.0000189251295523718,
        "seek": 49336,
        "start": 504.40000000000003,
        "temperature": 0,
        "text": " Ys also be an array.",
        "tokens": [
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        "compression_ratio": 1.855421686746988,
        "end": 510.22,
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        "seek": 49336,
        "start": 506.24,
        "temperature": 0,
        "text": " So, and then, whenever I click the mouse,",
        "tokens": [
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        "compression_ratio": 1.855421686746988,
        "end": 513.44,
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        "seek": 49336,
        "start": 512.04,
        "temperature": 0,
        "text": " I wanna say, oh, you know what I could do?",
        "tokens": [
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      {
        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 515.16,
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        "seek": 49336,
        "start": 513.44,
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        "text": " I could make those vectors,",
        "tokens": [
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        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 516.92,
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        "no_speech_prob": 0.0000189251295523718,
        "seek": 49336,
        "start": 515.16,
        "temperature": 0,
        "text": " let's make them separate arrays.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 518.28,
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        "text": " I think we're gonna, actually, I know we're gonna,",
        "tokens": [
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      {
        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 519.94,
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        "no_speech_prob": 0.0000189251295523718,
        "seek": 49336,
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        "temperature": 0,
        "text": " we're gonna wanna do that for a variety of reasons.",
        "tokens": [
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        "avg_logprob": -0.21298168467826584,
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        "end": 521.16,
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        "no_speech_prob": 0.0000189251295523718,
        "seek": 49336,
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        "temperature": 0,
        "text": " We're gonna keep those as separate arrays.",
        "tokens": [
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        "avg_logprob": -0.21298168467826584,
        "compression_ratio": 1.855421686746988,
        "end": 523.04,
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        "seek": 49336,
        "start": 521.16,
        "temperature": 0,
        "text": " So every time I click the mouse,",
        "tokens": [
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        "avg_logprob": -0.2726220231715257,
        "compression_ratio": 1.6457399103139014,
        "end": 527.24,
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        "no_speech_prob": 0.00001342000177828595,
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        "text": " I'm going to say Xs, push mouse X,",
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        "avg_logprob": -0.2726220231715257,
        "compression_ratio": 1.6457399103139014,
        "end": 528.92,
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        "seek": 52304,
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        "temperature": 0,
        "text": " Ys, push mouse Y.",
        "tokens": [
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        "avg_logprob": -0.2726220231715257,
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        "end": 530.12,
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        "temperature": 0,
        "text": " Ah, okay.",
        "tokens": [
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        "avg_logprob": -0.2726220231715257,
        "compression_ratio": 1.6457399103139014,
        "end": 533.06,
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        "seek": 52304,
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        "temperature": 0,
        "text": " Here's a little thing.",
        "tokens": [
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        "avg_logprob": -0.2726220231715257,
        "compression_ratio": 1.6457399103139014,
        "end": 537.0799999999999,
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        "seek": 52304,
        "start": 534.5999999999999,
        "temperature": 0,
        "text": " So this is our canvas, right?",
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        "start": 537.0799999999999,
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        "text": " This is my drawing of the canvas.",
        "tokens": [
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        "compression_ratio": 1.6457399103139014,
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        "temperature": 0,
        "text": " I know, now I'm having like a deja vu thing.",
        "tokens": [
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        "text": " I totally talked about this in the other video.",
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        "text": " The width is like 400, the height is 400,",
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        "text": " but I really wanna think of this as,",
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        "text": " I really wanna think of zero, zero down here,",
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        "text": " and maybe one being over here.",
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        "compression_ratio": 1.9324324324324325,
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        "temperature": 0,
        "text": " I wanna normalize everything between zero and one.",
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        "temperature": 0,
        "text": " Everything's just gonna work better if we do that.",
        "tokens": [
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        "compression_ratio": 1.9324324324324325,
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        "temperature": 0,
        "text": " So with Y pointing up.",
        "tokens": [
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        "compression_ratio": 1.9324324324324325,
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        "temperature": 0,
        "text": " So I'm gonna do a mapping.",
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        "compression_ratio": 1.9324324324324325,
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        "seek": 55196,
        "start": 561.2800000000001,
        "temperature": 0,
        "text": " So every Y value, that's pixel value between zero and height,",
        "tokens": [
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        "avg_logprob": -0.18493613901064376,
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        "end": 567.08,
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        "seek": 55196,
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        "temperature": 0,
        "text": " I'm gonna map between one and zero,",
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      {
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        "end": 569.32,
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        "seek": 55196,
        "start": 567.08,
        "temperature": 0,
        "text": " and every X value that's between zero and width,",
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        "no_speech_prob": 0.000006643431788688758,
        "seek": 55196,
        "start": 569.32,
        "temperature": 0,
        "text": " I'm gonna map between zero and one.",
        "tokens": [
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        "end": 572.84,
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        "seek": 55196,
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        "temperature": 0,
        "text": " So let's do that.",
        "tokens": [
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      },
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        "compression_ratio": 1.9324324324324325,
        "end": 577.84,
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        "no_speech_prob": 0.000006643431788688758,
        "seek": 55196,
        "start": 572.84,
        "temperature": 0,
        "text": " So I'm going to say, let X equal map mouse X,",
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        "compression_ratio": 1.68,
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        "no_speech_prob": 1.944432739264812e-7,
        "seek": 57784,
        "start": 578.0400000000001,
        "temperature": 0,
        "text": " which goes between zero and width,",
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        "no_speech_prob": 1.944432739264812e-7,
        "seek": 57784,
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        "temperature": 0,
        "text": " to between zero and one.",
        "tokens": [
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      {
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        "compression_ratio": 1.68,
        "end": 589.2,
        "id": 224,
        "no_speech_prob": 1.944432739264812e-7,
        "seek": 57784,
        "start": 584.2,
        "temperature": 0,
        "text": " Let Y, which is mouse Y, between zero and height,",
        "tokens": [
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        "compression_ratio": 1.68,
        "end": 592.6,
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        "no_speech_prob": 1.944432739264812e-7,
        "seek": 57784,
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        "temperature": 0,
        "text": " and have that go between one and zero,",
        "tokens": [
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        "compression_ratio": 1.68,
        "end": 595.08,
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        "no_speech_prob": 1.944432739264812e-7,
        "seek": 57784,
        "start": 592.6,
        "temperature": 0,
        "text": " and then push X and push Y.",
        "tokens": [
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        "text": " The other thing I wanna do is I just want to,",
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        "text": " you know, I'm gonna add a draw loop,",
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        "text": " and I wanna draw all those points.",
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        "text": " So I'm also now gonna say,",
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        "text": " stroke 255, stroke weight four,",
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        "avg_logprob": -0.2951297370754943,
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        "text": " for let I equal, what, huh?",
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        "text": " Let I equal zero, I is less than X dot length, I plus plus,",
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        "text": " and those are actually called Xs, Xs dot length.",
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        "text": " And what I'm gonna do is I'm gonna say,",
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        "text": " let pixel X equal map.",
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        "text": " I really should make just like,",
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        "text": " I'm probably gonna have to do this a lot,",
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        "text": " so I should probably make a function",
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        "text": " that just like normalize and un-normalize or de-normalize.",
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        "text": " Pix, PX equals map X's index I,",
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        "text": " which goes between zero and one, back to zero and width.",
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        "text": " So this is the reverse.",
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        "text": " PY, which maps Y, which goes between zero and one",
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        "text": " to height comma zero.",
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        "text": " And then I wanna say, point PX, PY.",
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        "text": " So I haven't done any, I haven't even,",
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        "text": " I'm not even using TensorFlow.js yet.",
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        "text": " I'm just kind of doing the stuff with P5 to draw things.",
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        "text": " So let's see if I'm getting the results that I want,",
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        "text": " which is whenever I click, I get the points there.",
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        "text": " Perfect.",
        "tokens": [
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        "text": " And I kind of wanna see them a bit more.",
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        "text": " Let's like really make it bigger.",
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        "text": " Great, that's like too big.",
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        "text": " Okay, great.",
        "tokens": [
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        "avg_logprob": -0.19079572504216974,
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        "seek": 66250,
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        "temperature": 0,
        "text": " So we can see, those are the points I'm clicking on.",
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        "avg_logprob": -0.19079572504216974,
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        "temperature": 0,
        "text": " Okay, so what's next?",
        "tokens": [
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      },
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        "avg_logprob": -0.19079572504216974,
        "compression_ratio": 1.6407407407407408,
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        "text": " I need a loss function, I need an optimizer.",
        "tokens": [
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        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
        "end": 692.62,
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        "no_speech_prob": 0.000013846001820638776,
        "seek": 69022,
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        "temperature": 0,
        "text": " Ah, oh, I need these.",
        "tokens": [
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        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
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        "text": " Let's make these.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
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        "no_speech_prob": 0.000013846001820638776,
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        "temperature": 0,
        "text": " So, cause I'm looking for somewhere",
        "tokens": [
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      },
      {
        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
        "end": 698.72,
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        "seek": 69022,
        "start": 695.4200000000001,
        "temperature": 0,
        "text": " where I need to get some TensorFlow.js stuff working.",
        "tokens": [
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        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
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        "seek": 69022,
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        "temperature": 0,
        "text": " So what I need is I need to have M and B.",
        "tokens": [
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        "temperature": 0,
        "text": " So let's figure that out.",
        "tokens": [
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        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
        "end": 709.38,
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        "no_speech_prob": 0.000013846001820638776,
        "seek": 69022,
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        "temperature": 0,
        "text": " So I'm going to create an M and a B.",
        "tokens": [
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      {
        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
        "end": 714.62,
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        "no_speech_prob": 0.000013846001820638776,
        "seek": 69022,
        "start": 710.5400000000001,
        "temperature": 0,
        "text": " And I'm not gonna initialize them in setup, up here.",
        "tokens": [
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      {
        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
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        "seek": 69022,
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        "temperature": 0,
        "text": " And I probably should be using const in various places here",
        "tokens": [
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      {
        "avg_logprob": -0.19950297621429944,
        "compression_ratio": 1.6265060240963856,
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        "no_speech_prob": 0.000013846001820638776,
        "seek": 69022,
        "start": 717.4200000000001,
        "temperature": 0,
        "text": " to protect myself from reassigning something by accident,",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 722.86,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
        "start": 720.5,
        "temperature": 0,
        "text": " but I'm gonna be loosey goosey and just use let.",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 726.42,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
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        "temperature": 0,
        "text": " You know, these could be const.",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 728.1800000000001,
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        "seek": 71986,
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        "temperature": 0,
        "text": " But anyway, I'm not gonna get into the whole let",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 730.3000000000001,
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        "seek": 71986,
        "start": 728.1800000000001,
        "temperature": 0,
        "text": " versus const thing, it makes me crazy.",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 735.3000000000001,
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        "seek": 71986,
        "start": 730.3000000000001,
        "temperature": 0,
        "text": " I'm gonna say up here, M equals TF scalar random one.",
        "tokens": [
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        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 739.46,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
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        "temperature": 0,
        "text": " So I'm gonna use the P5 random function",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 741.1,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
        "start": 739.46,
        "temperature": 0,
        "text": " to give me a random number between zero and one,",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 742.46,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
        "start": 741.1,
        "temperature": 0,
        "text": " cause I gotta start somewhere.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 744.72,
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        "no_speech_prob": 0.000017502909031463787,
        "seek": 71986,
        "start": 742.46,
        "temperature": 0,
        "text": " So this is kind of like initializing the weights",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 745.5600000000001,
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        "seek": 71986,
        "start": 744.72,
        "temperature": 0,
        "text": " of a neural network.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 746.38,
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        "text": " There is no neural network,",
        "tokens": [
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      {
        "avg_logprob": -0.2618025154903017,
        "compression_ratio": 1.7206896551724138,
        "end": 749.22,
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        "temperature": 0,
        "text": " I'm just doing, I'm just kind of optimizing this function,",
        "tokens": [
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        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
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        "seek": 74922,
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        "temperature": 0,
        "text": " Y equals MX plus B, but those are like weights, M and B.",
        "tokens": [
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      {
        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 754.1,
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        "no_speech_prob": 0.000011125604032713454,
        "seek": 74922,
        "start": 752.6600000000001,
        "temperature": 0,
        "text": " So I'm gonna initialize them randomly",
        "tokens": [
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      {
        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 756.14,
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        "text": " and scalar cause it's a single number.",
        "tokens": [
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        "end": 759.4200000000001,
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        "temperature": 0,
        "text": " So go back to my TensorFlow.js intro videos and you'll see.",
        "tokens": [
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        "seek": 74922,
        "start": 759.4200000000001,
        "temperature": 0,
        "text": " Then B, I'm gonna say the same thing, ah, but,",
        "tokens": [
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        "avg_logprob": -0.2417110744997752,
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        "end": 766.38,
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        "text": " these are the things that have to change, right?",
        "tokens": [
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      {
        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 769.7,
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        "temperature": 0,
        "text": " The data never changes, it's sort of fixed.",
        "tokens": [
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        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 771.74,
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        "seek": 74922,
        "start": 769.7,
        "temperature": 0,
        "text": " M and B change over time.",
        "tokens": [
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      },
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        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 775.14,
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        "seek": 74922,
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        "temperature": 0,
        "text": " I need to tweak those, which means they have to be variable.",
        "tokens": [
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        "avg_logprob": -0.2417110744997752,
        "compression_ratio": 1.684782608695652,
        "end": 777.82,
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        "no_speech_prob": 0.000011125604032713454,
        "seek": 74922,
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        "temperature": 0,
        "text": " They have to be able to change, which means,",
        "tokens": [
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      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 781.1,
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        "seek": 77782,
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        "temperature": 0,
        "text": " when I over here, I think what I write is TF variable,",
        "tokens": [
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      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 783.1800000000001,
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        "seek": 77782,
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        "temperature": 0,
        "text": " I wrap them in the TF variable.",
        "tokens": [
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      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 789.0600000000001,
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        "seek": 77782,
        "start": 784.1,
        "temperature": 0,
        "text": " So now I have M and B as TF variable, right?",
        "tokens": [
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        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 790.86,
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        "seek": 77782,
        "start": 789.0600000000001,
        "temperature": 0,
        "text": " Isn't it crazy, like you see this kind of code",
        "tokens": [
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        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 791.7800000000001,
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        "text": " and you're like, that looks like",
        "tokens": [
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        "compression_ratio": 1.726643598615917,
        "end": 793.46,
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        "text": " the craziest, scariest thing,",
        "tokens": [
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      },
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        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 795.7,
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        "seek": 77782,
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        "temperature": 0,
        "text": " but you realize like it's just like, make a number,",
        "tokens": [
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      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 798.5400000000001,
        "id": 299,
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        "seek": 77782,
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        "temperature": 0,
        "text": " and because we're in this like kind of lower level,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 801.74,
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        "no_speech_prob": 0.0000029944405923743034,
        "seek": 77782,
        "start": 798.5400000000001,
        "temperature": 0,
        "text": " working on the GPU land, I've gotta be very like specific.",
        "tokens": [
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        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 804.62,
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        "no_speech_prob": 0.0000029944405923743034,
        "seek": 77782,
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        "temperature": 0,
        "text": " Like this is a single number and it's gonna be variable,",
        "tokens": [
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      {
        "avg_logprob": -0.232750461526113,
        "compression_ratio": 1.726643598615917,
        "end": 807.34,
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        "no_speech_prob": 0.0000029944405923743034,
        "seek": 77782,
        "start": 804.62,
        "temperature": 0,
        "text": " but really it's just a random number.",
        "tokens": [
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        "avg_logprob": -0.2019420659767007,
        "compression_ratio": 1.7009345794392523,
        "end": 810.4200000000001,
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        "no_speech_prob": 3.41258612479578e-7,
        "seek": 80734,
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        "temperature": 0,
        "text": " Now, what do we need to do?",
        "tokens": [
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      },
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        "avg_logprob": -0.2019420659767007,
        "compression_ratio": 1.7009345794392523,
        "end": 813.94,
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        "no_speech_prob": 3.41258612479578e-7,
        "seek": 80734,
        "start": 810.4200000000001,
        "temperature": 0,
        "text": " We need to write, I don't think I actually said this,",
        "tokens": [
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        "text": " but I need to write a function called predict maybe,",
        "tokens": [
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      {
        "avg_logprob": -0.2019420659767007,
        "compression_ratio": 1.7009345794392523,
        "end": 825.1,
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        "no_speech_prob": 3.41258612479578e-7,
        "seek": 80734,
        "start": 820.1,
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        "text": " which takes in all of the Xs, just the Xs,",
        "tokens": [
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        "compression_ratio": 1.7009345794392523,
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        "start": 825.26,
        "temperature": 0,
        "text": " and gives me the Y predictions based on where the line is.",
        "tokens": [
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      {
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        "compression_ratio": 1.7009345794392523,
        "end": 830.34,
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        "no_speech_prob": 3.41258612479578e-7,
        "seek": 80734,
        "start": 828.58,
        "temperature": 0,
        "text": " Because I need to compare the Y predictions",
        "tokens": [
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      {
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        "compression_ratio": 1.7009345794392523,
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        "temperature": 0,
        "text": " to the actual Y values to get the mean squared error.",
        "tokens": [
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      {
        "avg_logprob": -0.2019420659767007,
        "compression_ratio": 1.7009345794392523,
        "end": 836.22,
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        "temperature": 0,
        "text": " So let's write that function.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21877328339997712,
        "compression_ratio": 1.6208530805687205,
        "end": 838.5,
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        "seek": 83622,
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        "temperature": 0,
        "text": " I'm putting these in like arbitrary places,",
        "tokens": [
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        "seek": 83622,
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        "temperature": 0,
        "text": " but I'm gonna write a function called predict.",
        "tokens": [
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        "avg_logprob": -0.21877328339997712,
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        "temperature": 0,
        "text": " And there what I need to do is I need to have some Xs",
        "tokens": [
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        "avg_logprob": -0.21877328339997712,
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        "text": " and I need to return some Ys.",
        "tokens": [
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        "avg_logprob": -0.21877328339997712,
        "compression_ratio": 1.6208530805687205,
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        "temperature": 0,
        "text": " I think that's the idea, right?",
        "tokens": [
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        "compression_ratio": 1.6208530805687205,
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        "temperature": 0,
        "text": " Yes, so I don't wanna just predict one value,",
        "tokens": [
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        "text": " I wanna predict a bunch.",
        "tokens": [
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        "temperature": 0,
        "text": " So the Xs, here's the thing.",
        "tokens": [
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        "compression_ratio": 1.6208530805687205,
        "end": 864.98,
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        "temperature": 0,
        "text": " So if I call this function, the Xs,",
        "tokens": [
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        "compression_ratio": 1.536144578313253,
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        "no_speech_prob": 0.000004860447006649338,
        "seek": 86498,
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        "temperature": 0,
        "text": " if they're just a plain array,",
        "tokens": [
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      {
        "avg_logprob": -0.30145972402472243,
        "compression_ratio": 1.536144578313253,
        "end": 869.22,
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        "no_speech_prob": 0.000004860447006649338,
        "seek": 86498,
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        "temperature": 0,
        "text": " I need to make it into a tensor.",
        "tokens": [
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        "compression_ratio": 1.536144578313253,
        "end": 873.34,
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        "seek": 86498,
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        "temperature": 0,
        "text": " So I'm gonna call it const TF Xs.",
        "tokens": [
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        "temperature": 0,
        "text": " That might be a bad, it's tensor.",
        "tokens": [
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        "seek": 86498,
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        "temperature": 0,
        "text": " And this is a 1D tensor, tensor 1D.",
        "tokens": [
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        "compression_ratio": 1.536144578313253,
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        "seek": 86498,
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        "temperature": 0,
        "text": " Oh, TF, tensor 1D, I think this will do it, Xs, right?",
        "tokens": [
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        "end": 891.38,
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        "temperature": 0,
        "text": " I need to turn it into a tensor.",
        "tokens": [
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        "avg_logprob": -0.2663939570037412,
        "compression_ratio": 1.4055944055944056,
        "end": 897.38,
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        "seek": 89138,
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        "temperature": 0,
        "text": " And then I need to have the formula for a line.",
        "tokens": [
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        "compression_ratio": 1.4055944055944056,
        "end": 905.18,
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        "seek": 89138,
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        "temperature": 0,
        "text": " So I need to say, which is Y equals MX plus B.",
        "tokens": [
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        "compression_ratio": 1.4055944055944056,
        "end": 911.26,
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        "seek": 89138,
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        "temperature": 0,
        "text": " So what I would be doing is I would be saying TF Xs",
        "tokens": [
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        "temperature": 0,
        "text": " multiplied, is it MUL or MULT?",
        "tokens": [
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        "compression_ratio": 1.4055944055944056,
        "end": 918.2,
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        "no_speech_prob": 0.00000139254632358643,
        "seek": 89138,
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        "temperature": 0,
        "text": " Multiplied by M plus B.",
        "tokens": [
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        "compression_ratio": 1.6105769230769231,
        "end": 922.88,
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        "seek": 91820,
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        "temperature": 0,
        "text": " Right, this is the idea.",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 926.72,
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        "no_speech_prob": 0.00000788925990491407,
        "seek": 91820,
        "start": 924,
        "temperature": 0,
        "text": " If I'm getting just a plain array of numbers,",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 928.96,
        "id": 334,
        "no_speech_prob": 0.00000788925990491407,
        "seek": 91820,
        "start": 926.72,
        "temperature": 0,
        "text": " I turn them into a tensor,",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 931.2,
        "id": 335,
        "no_speech_prob": 0.00000788925990491407,
        "seek": 91820,
        "start": 928.96,
        "temperature": 0,
        "text": " then I apply the formula for a line,",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 933.2800000000001,
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        "temperature": 0,
        "text": " and these are the predictions, the Ys.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 935.7,
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        "seek": 91820,
        "start": 933.2800000000001,
        "temperature": 0,
        "text": " I guess, I don't like my naming here.",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 940.36,
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        "no_speech_prob": 0.00000788925990491407,
        "seek": 91820,
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        "temperature": 0,
        "text": " I'm just gonna call this X,",
        "tokens": [
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      {
        "avg_logprob": -0.24654494198885832,
        "compression_ratio": 1.6105769230769231,
        "end": 942.46,
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        "seek": 91820,
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        "temperature": 0,
        "text": " and maybe I'll call this Xs, I don't know.",
        "tokens": [
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        "compression_ratio": 1.6105769230769231,
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        "seek": 91820,
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        "temperature": 0,
        "text": " I have to think about, I'll come back to this later.",
        "tokens": [
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        "temperature": 0,
        "text": " Okay, so I have that.",
        "tokens": [
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        "seek": 94820,
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        "temperature": 0,
        "text": " Now, let's go back and look at the things that I need.",
        "tokens": [
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        "text": " So I have this predict function, I have a data set.",
        "tokens": [
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        "text": " Ah, I need a loss function.",
        "tokens": [
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      {
        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
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        "seek": 94820,
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        "temperature": 0,
        "text": " Need a loss function, and I need a,",
        "tokens": [
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      {
        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
        "end": 969.72,
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        "text": " oh, before we do the loss function,",
        "tokens": [
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      {
        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
        "end": 972.12,
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        "seek": 94820,
        "start": 969.72,
        "temperature": 0,
        "text": " let's create the optimizer and the learning rate.",
        "tokens": [
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        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
        "end": 973.76,
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        "no_speech_prob": 0.0000010845166116268956,
        "seek": 94820,
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        "temperature": 0,
        "text": " So this is what's wonderful about",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
        "end": 976.44,
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        "text": " working with TensorFlow.js.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21463894021922145,
        "compression_ratio": 1.7255813953488373,
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        "no_speech_prob": 0.0000010845166116268956,
        "seek": 94820,
        "start": 976.44,
        "temperature": 0,
        "text": " When I say make the optimizer,",
        "tokens": [
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      {
        "avg_logprob": -0.2288155162006343,
        "compression_ratio": 1.5740740740740742,
        "end": 981.04,
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        "seek": 97792,
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        "text": " I just mean make a tf.optimizer.",
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        "seek": 97792,
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        "text": " It exists, it'll do this math for us.",
        "tokens": [
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        "avg_logprob": -0.2288155162006343,
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        "temperature": 0,
        "text": " So let's go to the,",
        "tokens": [
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      },
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        "avg_logprob": -0.2288155162006343,
        "compression_ratio": 1.5740740740740742,
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        "text": " this is not something that I covered in my other videos,",
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        "temperature": 0,
        "text": " so let's go look for optimizer,",
        "tokens": [
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      {
        "avg_logprob": -0.2288155162006343,
        "compression_ratio": 1.5740740740740742,
        "end": 991.7199999999999,
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        "seek": 97792,
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        "temperature": 0,
        "text": " and I want an optimizer.",
        "tokens": [
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        "end": 994.5999999999999,
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        "text": " Now, there's all these different kinds of optimizers.",
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        "text": " SGD, stochastic gradient descent.",
        "tokens": [
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        "text": " This means the idea of slowly adjusting M and B",
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        "text": " to minimize the loss function,",
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        "compression_ratio": 1.804,
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        "text": " and I've covered this in more detail in the other videos.",
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        "text": " So I'm gonna click on that, and I'm gonna look here,",
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        "text": " and this is basically what I need to do.",
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        "text": " All right, we got the code right here.",
        "tokens": [
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        "text": " Look, there's even a, look at this.",
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        "text": " Oh my goodness, there's like some stuff here",
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        "text": " we can really use.",
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        "text": " So I'm gonna grab this,",
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        "text": " and I'm gonna put this up here.",
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        "temperature": 0,
        "text": " So I want a learning rate,",
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        "compression_ratio": 1.804,
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        "seek": 100392,
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        "text": " and I'm gonna have a much bigger learning rate",
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        "temperature": 0,
        "text": " to start with, and I want an optimizer.",
        "tokens": [
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        "temperature": 0,
        "text": " So now I have a learning rate and an optimizer,",
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        "text": " and the optimizer is doing stochastic gradient descent.",
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        "temperature": 0,
        "text": " So I have learning rate, optimizer.",
        "tokens": [
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        "temperature": 0,
        "text": " Now I need that loss function.",
        "tokens": [
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      },
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        "temperature": 0,
        "text": " I need the loss function.",
        "tokens": [
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      {
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        "seek": 103288,
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        "temperature": 0,
        "text": " Okay, the loss function is something I'm gonna write, loss,",
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        "temperature": 0,
        "text": " and actually, let's go back to here.",
        "tokens": [
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        "seek": 103288,
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        "temperature": 0,
        "text": " So look at this.",
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      },
      {
        "avg_logprob": -0.17006885118720946,
        "compression_ratio": 1.8272727272727274,
        "end": 1060,
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        "seek": 103288,
        "start": 1056.8000000000002,
        "temperature": 0,
        "text": " So this is the fancy ES6 way of writing a function,",
        "tokens": [
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        "compression_ratio": 1.9017094017094016,
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        "no_speech_prob": 9.874638635665178e-7,
        "seek": 106000,
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        "temperature": 0,
        "text": " but I'm gonna write it in a less fancy way,",
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        "seek": 106000,
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        "text": " and I'm gonna do this.",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
        "end": 1069.56,
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        "seek": 106000,
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        "temperature": 0,
        "text": " So what I want is I need the loss function.",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
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        "text": " I have some predictions, and I have some labels.",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
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        "temperature": 0,
        "text": " So these are, the predictions are the y values I'm getting",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
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        "text": " from the predict function.",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
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        "text": " The labels are the actual y values that are part of this,",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
        "end": 1086.28,
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        "start": 1084.16,
        "temperature": 0,
        "text": " and by the way, I'm gonna have to do memory management.",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
        "compression_ratio": 1.9017094017094016,
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        "no_speech_prob": 9.874638635665178e-7,
        "seek": 106000,
        "start": 1086.28,
        "temperature": 0,
        "text": " Don't worry, if you're screaming at me",
        "tokens": [
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      {
        "avg_logprob": -0.1892900091456616,
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        "end": 1089.48,
        "id": 391,
        "no_speech_prob": 9.874638635665178e-7,
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        "temperature": 0,
        "text": " that I haven't worked about memory management,",
        "tokens": [
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        "avg_logprob": -0.2634195467320884,
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        "end": 1091.72,
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        "no_speech_prob": 0.0000042228330130456015,
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        "text": " I'm just gonna do that later.",
        "tokens": [
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      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1096.2,
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        "seek": 108948,
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        "temperature": 0,
        "text": " So what I wanna do is say return",
        "tokens": [
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        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1098.3,
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        "text": " the predictions minus the labels.",
        "tokens": [
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      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1099.72,
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        "text": " That makes sense, right?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1103.22,
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        "seek": 108948,
        "start": 1099.72,
        "temperature": 0,
        "text": " Because I said here, when I said mean squared error",
        "tokens": [
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      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1106.48,
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        "temperature": 0,
        "text": " is the predictions, like the guess, minus the labels,",
        "tokens": [
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      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1108.72,
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        "seek": 108948,
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        "temperature": 0,
        "text": " which is the actual y squared.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2634195467320884,
        "compression_ratio": 1.7396449704142012,
        "end": 1114.16,
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        "no_speech_prob": 0.0000042228330130456015,
        "seek": 108948,
        "start": 1111.1200000000001,
        "temperature": 0,
        "text": " And so predictions minus the labels",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2821443231256158,
        "compression_ratio": 1.6736401673640167,
        "end": 1117.52,
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        "no_speech_prob": 0.000008801101103017572,
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        "temperature": 0,
        "text": " is the predictions squared,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2821443231256158,
        "compression_ratio": 1.6736401673640167,
        "end": 1121.4,
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        "seek": 111416,
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        "temperature": 0,
        "text": " and then take the mean of them.",
        "tokens": [
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      {
        "avg_logprob": -0.2821443231256158,
        "compression_ratio": 1.6736401673640167,
        "end": 1122.64,
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        "temperature": 0,
        "text": " Look at this.",
        "tokens": [
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      {
        "avg_logprob": -0.2821443231256158,
        "compression_ratio": 1.6736401673640167,
        "end": 1125.16,
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        "no_speech_prob": 0.000008801101103017572,
        "seek": 111416,
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        "temperature": 0,
        "text": " All of these mathematical operations",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2821443231256158,
        "compression_ratio": 1.6736401673640167,
        "end": 1128.92,
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        "text": " are inside of TensorFlow.js, and you can chain them.",
        "tokens": [
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      {
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        "compression_ratio": 1.6736401673640167,
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        "temperature": 0,
        "text": " So predictions is a tensor, labels is a tensor.",
        "tokens": [
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        "temperature": 0,
        "text": " All of these, remember, they're just gonna keep producing",
        "tokens": [
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        "avg_logprob": -0.2821443231256158,
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        "text": " new tensors, and I'm gonna have to tidy and clean",
        "tokens": [
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      {
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        "text": " all this stuff up for memory management,",
        "tokens": [
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        "compression_ratio": 1.6736401673640167,
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        "temperature": 0,
        "text": " but again, I'll worry about that later.",
        "tokens": [
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        "temperature": 0,
        "text": " So now I have the loss function.",
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        "text": " All right, well, what is it that I wanna do?",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
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        "text": " Every time, so let's say, I think I'm actually like,",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1152.2,
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        "text": " I have everything.",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1155.52,
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        "no_speech_prob": 0.000004565965809888439,
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        "temperature": 0,
        "text": " I have the loss function, I have the data,",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1157.5,
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        "no_speech_prob": 0.000004565965809888439,
        "seek": 114276,
        "start": 1155.52,
        "temperature": 0,
        "text": " I have the optimizer, I have a predict function,",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1159,
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        "no_speech_prob": 0.000004565965809888439,
        "seek": 114276,
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        "temperature": 0,
        "text": " I have a learning rate, I can minimize,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1160.94,
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        "seek": 114276,
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        "temperature": 0,
        "text": " well, oh, this I haven't done.",
        "tokens": [
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      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1163.68,
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        "no_speech_prob": 0.000004565965809888439,
        "seek": 114276,
        "start": 1160.94,
        "temperature": 0,
        "text": " So the training, the actual training,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1165.32,
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        "no_speech_prob": 0.000004565965809888439,
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        "temperature": 0,
        "text": " what does it mean to train it?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1168.2,
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        "no_speech_prob": 0.000004565965809888439,
        "seek": 114276,
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        "temperature": 0,
        "text": " To train it means minimize the loss function",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18143631660774961,
        "compression_ratio": 1.9357429718875503,
        "end": 1172.24,
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        "no_speech_prob": 0.000004565965809888439,
        "seek": 114276,
        "start": 1168.2,
        "temperature": 0,
        "text": " with the optimizer and adjusting M and B based on that.",
        "tokens": [
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        "compression_ratio": 1.7151898734177216,
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        "temperature": 0,
        "text": " All right, so let's see if we can make that.",
        "tokens": [
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        "seek": 117224,
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        "temperature": 0,
        "text": " I have a feeling that was in that page that I went to,",
        "tokens": [
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        "avg_logprob": -0.24103608188858952,
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        "seek": 117224,
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        "temperature": 0,
        "text": " so maybe I could just copy it from there.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
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        "end": 1181.72,
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        "temperature": 0,
        "text": " I'm kind of, this is like, it totally is.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
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        "temperature": 0,
        "text": " That's fine, I'm gonna happily cheat here.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
        "end": 1186.16,
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        "temperature": 0,
        "text": " It was in that example, thank you very much.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
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        "no_speech_prob": 0.00002247403608635068,
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        "temperature": 0,
        "text": " Thank you, TensorFlow.js documentation.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
        "end": 1192.28,
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        "seek": 117224,
        "start": 1190.68,
        "temperature": 0,
        "text": " And so I'm gonna just put this in draw.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
        "end": 1194.5,
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        "temperature": 0,
        "text": " Like, every time through draw, I'm gonna minimize.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
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        "temperature": 0,
        "text": " So let's look at this, oh, look at this.",
        "tokens": [
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        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
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        "temperature": 0,
        "text": " Okay, so this is a little different.",
        "tokens": [
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      {
        "avg_logprob": -0.24103608188858952,
        "compression_ratio": 1.7151898734177216,
        "end": 1202.08,
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        "seek": 117224,
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        "temperature": 0,
        "text": " So first of all, this is using nice, fancy ES6 arrow notation",
        "tokens": [
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      {
        "avg_logprob": -0.24326780319213867,
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        "end": 1203.76,
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        "temperature": 0,
        "text": " which I'm somewhat happy about.",
        "tokens": [
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        "avg_logprob": -0.24326780319213867,
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        "text": " But let me just write a function here called train.",
        "tokens": [
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        "avg_logprob": -0.24326780319213867,
        "compression_ratio": 1.6036866359447004,
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        "temperature": 0,
        "text": " And the idea of the train function is to execute the loss",
        "tokens": [
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      {
        "avg_logprob": -0.24326780319213867,
        "compression_ratio": 1.6036866359447004,
        "end": 1217.48,
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        "temperature": 0,
        "text": " with the predictions and the actual y's.",
        "tokens": [
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        "end": 1222.52,
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        "temperature": 0,
        "text": " Okay, so here, what I'm really doing",
        "tokens": [
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        "avg_logprob": -0.24326780319213867,
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        "text": " is minimizing the train.",
        "tokens": [
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        "temperature": 0,
        "text": " That's weird, this isn't really,",
        "tokens": [
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        "text": " no, training would be doing this.",
        "tokens": [
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        "temperature": 0,
        "text": " So this is a terrible name for this.",
        "tokens": [
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        "temperature": 0,
        "text": " And actually, this is silly for me to even name this.",
        "tokens": [
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        "temperature": 0,
        "text": " It really makes sense for me to just make this",
        "tokens": [
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      {
        "avg_logprob": -0.22061465079324288,
        "compression_ratio": 1.7280334728033473,
        "end": 1240.88,
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        "text": " an anonymous function and that what I'm minimizing is this.",
        "tokens": [
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        "avg_logprob": -0.22061465079324288,
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        "text": " This is what I wanna minimize, the loss function.",
        "tokens": [
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        "avg_logprob": -0.22061465079324288,
        "compression_ratio": 1.7280334728033473,
        "end": 1248.7,
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        "temperature": 0,
        "text": " But if I wanna be nice and ES6-like with my arrow notation,",
        "tokens": [
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        "avg_logprob": -0.22061465079324288,
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        "end": 1251.92,
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        "seek": 123064,
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        "temperature": 0,
        "text": " which I think by the fact that I'm using TensorFlow.js",
        "tokens": [
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        "avg_logprob": -0.22061465079324288,
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        "end": 1253.72,
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        "temperature": 0,
        "text": " and you can watch my arrow notation function",
        "tokens": [
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        "avg_logprob": -0.22061465079324288,
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        "text": " if this is, I can kind of get rid of a lot",
        "tokens": [
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        "text": " of the extra stuff here and this should be good.",
        "tokens": [
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      {
        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
        "end": 1263.58,
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        "temperature": 0,
        "text": " So I just wanna minimize the loss function.",
        "tokens": [
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        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
        "end": 1264.66,
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        "temperature": 0,
        "text": " Now, here's the thing.",
        "tokens": [
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        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
        "end": 1268.82,
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        "no_speech_prob": 0.0000132119585032342,
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        "temperature": 0,
        "text": " These have to be tensors, right?",
        "tokens": [
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        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
        "end": 1273.16,
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        "no_speech_prob": 0.0000132119585032342,
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        "temperature": 0,
        "text": " The loss function requires predictions and labels.",
        "tokens": [
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        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
        "end": 1274.18,
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        "no_speech_prob": 0.0000132119585032342,
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        "temperature": 0,
        "text": " They have to be tensors.",
        "tokens": [
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      },
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        "compression_ratio": 1.6833333333333333,
        "end": 1278.02,
        "id": 457,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 125650,
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        "temperature": 0,
        "text": " And if you remember, my x's and y's aren't tensors.",
        "tokens": [
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        "temperature": 0,
        "text": " When I call the predict function with the x's,",
        "tokens": [
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        "avg_logprob": -0.20779941121085746,
        "compression_ratio": 1.6833333333333333,
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        "temperature": 0,
        "text": " it gives me back a tensor.",
        "tokens": [
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        "end": 1286.38,
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        "temperature": 0,
        "text": " So that, I can't believe I haven't run this code yet.",
        "tokens": [
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        "text": " This is a terrible thing.",
        "tokens": [
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        "text": " Usually I try to run my code incrementally all the time.",
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        "text": " I guess I've forgotten to do that.",
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        "text": " So probably people in the chat are telling me",
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        "text": " about mistakes I'm making.",
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        "temperature": 0,
        "text": " So this is a tensor, but this is still a plain array.",
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        "text": " So what I need to do is say constant",
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        "text": " and I gotta rethink the naming.",
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        "text": " Maybe somebody in the chat has an idea for me.",
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        "text": " I think what I actually should do, I have an idea.",
        "tokens": [
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        "end": 1312.22,
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        "seek": 128638,
        "start": 1309.6200000000001,
        "temperature": 0,
        "text": " Permit me a moment of refactoring.",
        "tokens": [
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        "text": " X valves, y valves.",
        "tokens": [
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        "avg_logprob": -0.1806561183003546,
        "compression_ratio": 1.587378640776699,
        "end": 1316.76,
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        "no_speech_prob": 8.714333716852707e-7,
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        "temperature": 0,
        "text": " So I think when it's not a tensor,",
        "tokens": [
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        "text": " I'm just gonna call it like x underscore valves",
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        "text": " because that's gonna help me remember.",
        "tokens": [
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        "text": " So x valves, y valves.",
        "tokens": [
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        "temperature": 0,
        "text": " And then whenever I say, and this should be x.",
        "tokens": [
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        "temperature": 0,
        "text": " Whenever I say xs or ys, that's really a tensor.",
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        "temperature": 0,
        "text": " I guess I could have done txs.",
        "tokens": [
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        "temperature": 0,
        "text": " So here, what I'm doing is predicting from the x valves",
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        "text": " and then ys is tf.tensor1dy valves.",
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        "temperature": 0,
        "text": " So I need to create that tensor",
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        "text": " and now I can minimize the loss",
        "tokens": [
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      {
        "avg_logprob": -0.2200645742745235,
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        "seek": 134010,
        "start": 1350.06,
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        "text": " with predicting from the x valves and the y valves.",
        "tokens": [
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        "text": " Okay, so this is good.",
        "tokens": [
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        "text": " Let's just run this.",
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        "text": " All right, I'm from the future, different day,",
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      {
        "avg_logprob": -0.2200645742745235,
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        "text": " different clothes, I'm breaking into this video",
        "tokens": [
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        "text": " to mention something really important",
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      },
      {
        "avg_logprob": -0.2200645742745235,
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        "text": " that I didn't actually mention",
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      },
      {
        "avg_logprob": -0.2200645742745235,
        "compression_ratio": 1.6451612903225807,
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        "text": " when I recorded the coding challenge originally.",
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        "temperature": 0,
        "text": " What is that optimize function doing?",
        "tokens": [
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      },
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        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
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        "seek": 136970,
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        "temperature": 0,
        "text": " How does it actually work?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
        "end": 1375.5,
        "id": 494,
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        "seek": 136970,
        "start": 1372.74,
        "temperature": 0,
        "text": " And we need to look at the TensorFlow.js documentation",
        "tokens": [
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      {
        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
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        "temperature": 0,
        "text": " to see, so let me, I'm bringing my laptop back up here",
        "tokens": [
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      {
        "avg_logprob": -0.21882133197067374,
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        "text": " and I'm gonna switch over here.",
        "tokens": [
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        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
        "end": 1382.02,
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        "seek": 136970,
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        "temperature": 0,
        "text": " So I've got the code from the past, me in the future.",
        "tokens": [
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        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
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        "seek": 136970,
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        "temperature": 0,
        "text": " This is the part that I'm talking about.",
        "tokens": [
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        "avg_logprob": -0.21882133197067374,
        "compression_ratio": 1.6560283687943262,
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        "temperature": 0,
        "text": " Well, how is this going to adjust m and b?",
        "tokens": [
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        "compression_ratio": 1.6560283687943262,
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        "temperature": 0,
        "text": " Those are the, these are the parameters,",
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        "text": " the weights, the variables that we need to adjust",
        "tokens": [
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        "seek": 136970,
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        "text": " to minimize that loss function.",
        "tokens": [
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        "avg_logprob": -0.23500591624866832,
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        "text": " But I'm not anywhere in here saying,",
        "tokens": [
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        "avg_logprob": -0.23500591624866832,
        "compression_ratio": 1.638655462184874,
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        "text": " those are the variables to work with.",
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        "compression_ratio": 1.638655462184874,
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        "seek": 139746,
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        "text": " Well, this is part of what TensorFlow.js does natively.",
        "tokens": [
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        "avg_logprob": -0.23500591624866832,
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        "text": " The fact that I made these up here, tf variables,",
        "tokens": [
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        "seek": 139746,
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        "temperature": 0,
        "text": " means those are variables that can be adjusted.",
        "tokens": [
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        "avg_logprob": -0.23500591624866832,
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        "seek": 139746,
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        "temperature": 0,
        "text": " And if we look here at the TensorFlow documentation,",
        "tokens": [
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      {
        "avg_logprob": -0.23500591624866832,
        "compression_ratio": 1.638655462184874,
        "end": 1418.8600000000001,
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        "seek": 139746,
        "start": 1417,
        "temperature": 0,
        "text": " you'll see what does minimize do?",
        "tokens": [
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      {
        "avg_logprob": -0.23500591624866832,
        "compression_ratio": 1.638655462184874,
        "end": 1422.82,
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        "seek": 139746,
        "start": 1418.8600000000001,
        "temperature": 0,
        "text": " It executes this function f, that is,",
        "tokens": [
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      {
        "avg_logprob": -0.23500591624866832,
        "compression_ratio": 1.638655462184874,
        "end": 1427.06,
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        "seek": 139746,
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        "temperature": 0,
        "text": " sorry, that is this function, right?",
        "tokens": [
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        "avg_logprob": -0.18044970806379965,
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        "text": " This whole function here.",
        "tokens": [
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        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1431.22,
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        "seek": 142706,
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        "temperature": 0,
        "text": " And by the way, the return here is implicit",
        "tokens": [
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      {
        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1432.78,
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        "seek": 142706,
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        "temperature": 0,
        "text": " because I'm using the arrow function.",
        "tokens": [
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        "temperature": 0,
        "text": " So it minimizes the output of that function,",
        "tokens": [
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      {
        "avg_logprob": -0.18044970806379965,
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        "temperature": 0,
        "text": " tries to get it lower by computing the gradients",
        "tokens": [
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      {
        "avg_logprob": -0.18044970806379965,
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        "temperature": 0,
        "text": " with respect to a list of all trainable variables",
        "tokens": [
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      {
        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1443.58,
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        "text": " provided by var list.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18044970806379965,
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        "text": " Guess what?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1446.8799999999999,
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        "seek": 142706,
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        "temperature": 0,
        "text": " I didn't provide a list of trainable variables.",
        "tokens": [
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        "end": 1448.62,
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        "no_speech_prob": 0.00008888079173630103,
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        "temperature": 0,
        "text": " If no list is provided,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1450.82,
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        "no_speech_prob": 0.00008888079173630103,
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        "temperature": 0,
        "text": " it defaults to all trainable variables.",
        "tokens": [
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        "avg_logprob": -0.18044970806379965,
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        "end": 1451.86,
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        "temperature": 0,
        "text": " And that's what's going on.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.18044970806379965,
        "compression_ratio": 1.8464566929133859,
        "end": 1453.62,
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        "temperature": 0,
        "text": " That's what I did in this coding challenge.",
        "tokens": [
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      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1457.8999999999999,
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        "no_speech_prob": 0.0000023320740183407906,
        "seek": 145362,
        "start": 1453.62,
        "temperature": 0,
        "text": " These are all the trainable variables in my system.",
        "tokens": [
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      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1460.58,
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        "no_speech_prob": 0.0000023320740183407906,
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        "temperature": 0,
        "text": " If I wanted to only use m or only use b,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1462.06,
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        "no_speech_prob": 0.0000023320740183407906,
        "seek": 145362,
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        "temperature": 0,
        "text": " I could put those in a list.",
        "tokens": [
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        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1463.78,
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        "seek": 145362,
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        "temperature": 0,
        "text": " So that's all I have to say about that.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20585432285215796,
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        "end": 1467.82,
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        "no_speech_prob": 0.0000023320740183407906,
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        "temperature": 0,
        "text": " I'm going to fade now back into the other video,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1469.4599999999998,
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        "seek": 145362,
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        "temperature": 0,
        "text": " lower this and back up,",
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      },
      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1471.62,
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        "seek": 145362,
        "start": 1469.4599999999998,
        "temperature": 0,
        "text": " and it's going to keep going where I debug",
        "tokens": [
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      {
        "avg_logprob": -0.20585432285215796,
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        "seek": 145362,
        "start": 1471.62,
        "temperature": 0,
        "text": " and have all sorts of other problems.",
        "tokens": [
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      {
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        "no_speech_prob": 0.0000023320740183407906,
        "seek": 145362,
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        "temperature": 0,
        "text": " Goodbye.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20585432285215796,
        "compression_ratio": 1.5912698412698412,
        "end": 1480.1799999999998,
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        "no_speech_prob": 0.0000023320740183407906,
        "seek": 145362,
        "start": 1476.5,
        "temperature": 0,
        "text": " Okay, predict.sub.squared is not a function at loss",
        "tokens": [
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      {
        "avg_logprob": -0.20585432285215796,
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        "text": " at optimize or minimize.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1483.76,
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        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1481.74,
        "temperature": 0,
        "text": " So what do I have wrong here?",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1486.46,
        "id": 537,
        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1484.78,
        "temperature": 0,
        "text": " In my loss function,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1492.42,
        "id": 538,
        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1487.42,
        "temperature": 0,
        "text": " sub labels.squared.mean.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1496.1200000000001,
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        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1494.58,
        "temperature": 0,
        "text": " Oh, you know what it is?",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1500.42,
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        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1498.34,
        "temperature": 0,
        "text": " There's nothing in the arrays at the beginning.",
        "tokens": [
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        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1501.84,
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        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1500.42,
        "temperature": 0,
        "text": " They have zero things in them.",
        "tokens": [
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        ]
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      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1504,
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        "seek": 148174,
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        "temperature": 0,
        "text": " So a couple of things, one is I could put something in it,",
        "tokens": [
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      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1505.94,
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        "seek": 148174,
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        "temperature": 0,
        "text": " but I think I probably should just say,",
        "tokens": [
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      {
        "avg_logprob": -0.27009698776971724,
        "compression_ratio": 1.5296803652968036,
        "end": 1510.94,
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        "no_speech_prob": 0.0000011911081401194679,
        "seek": 148174,
        "start": 1505.94,
        "temperature": 0,
        "text": " I shouldn't do it only if x.length is greater than zero.",
        "tokens": [
          51574,
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      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1515.06,
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        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1512.02,
        "temperature": 0,
        "text": " So this is definitely, then do I want to bother with,",
        "tokens": [
          50378,
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1516.66,
        "id": 546,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1515.06,
        "temperature": 0,
        "text": " do I want to bother with doing any of this?",
        "tokens": [
          50530,
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1518.02,
        "id": 547,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1516.66,
        "temperature": 0,
        "text": " If there's no values in there,",
        "tokens": [
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          311,
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          456,
          11,
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1520.66,
        "id": 548,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1518.02,
        "temperature": 0,
        "text": " like calling predict and stuff with an empty array,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1522.14,
        "id": 549,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1520.66,
        "temperature": 0,
        "text": " I think it's going to cause problems.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1523.38,
        "id": 550,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1522.14,
        "temperature": 0,
        "text": " That makes sense.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1524.68,
        "id": 551,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1523.38,
        "temperature": 0,
        "text": " All right, let's try this.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1528.88,
        "id": 552,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1525.58,
        "temperature": 0,
        "text": " X is not defined, x vals, my naming.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1533.66,
        "id": 553,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1530.16,
        "temperature": 0,
        "text": " Okay, sketch 45.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31761880354447797,
        "compression_ratio": 1.5674418604651164,
        "end": 1535.06,
        "id": 554,
        "no_speech_prob": 0.000005594337380898651,
        "seek": 151174,
        "start": 1533.66,
        "temperature": 0,
        "text": " Ah, this is x vals.",
        "tokens": [
          51460,
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      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1540.62,
        "id": 555,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
        "start": 1535.62,
        "temperature": 0,
        "text": " And this is x vals, y vals.",
        "tokens": [
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      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1543.8999999999999,
        "id": 556,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
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        "temperature": 0,
        "text": " Okay, that should be good.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1547,
        "id": 557,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
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        "temperature": 0,
        "text": " All right, let's try this.",
        "tokens": [
          50852,
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      },
      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1553.02,
        "id": 558,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
        "start": 1550.7,
        "temperature": 0,
        "text": " This is not square, being told in the chat,",
        "tokens": [
          51146,
          639,
          307,
          406,
          3732,
          11,
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      },
      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1556.8999999999999,
        "id": 559,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
        "start": 1553.02,
        "temperature": 0,
        "text": " breaking news, that this is actually.squared,",
        "tokens": [
          51262,
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      },
      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1558.8999999999999,
        "id": 560,
        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
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        "temperature": 0,
        "text": " not.squared, is that right?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.3430559662566788,
        "compression_ratio": 1.5256410256410255,
        "end": 1564.7,
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        "no_speech_prob": 0.000013211956684244797,
        "seek": 153506,
        "start": 1561.6599999999999,
        "temperature": 0,
        "text": " Yeah, oh, it's square, a.square, okay.",
        "tokens": [
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      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1566.4199999999998,
        "id": 562,
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        "seek": 156506,
        "start": 1565.58,
        "temperature": 0,
        "text": " Okay.",
        "tokens": [
          50390,
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1571.02,
        "id": 563,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1570.1799999999998,
        "temperature": 0,
        "text": " There we go, okay.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1573.74,
        "id": 564,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1571.02,
        "temperature": 0,
        "text": " So things are going and there's no, I don't have any,",
        "tokens": [
          50662,
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      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1578.74,
        "id": 565,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1573.74,
        "temperature": 0,
        "text": " like I could look at, that's m, m, m.print, right?",
        "tokens": [
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      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1583.34,
        "id": 566,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1581.94,
        "temperature": 0,
        "text": " So you can see it's changing.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1584.8999999999999,
        "id": 567,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1583.34,
        "temperature": 0,
        "text": " It's actually like training it.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1586.3999999999999,
        "id": 568,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1584.8999999999999,
        "temperature": 0,
        "text": " Like the value of m is changing.",
        "tokens": [
          51356,
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1588.1399999999999,
        "id": 569,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1586.3999999999999,
        "temperature": 0,
        "text": " So everything's going and working.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1591.06,
        "id": 570,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1588.1399999999999,
        "temperature": 0,
        "text": " The problem is I'm not seeing the results.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.25013566900182654,
        "compression_ratio": 1.5555555555555556,
        "end": 1592.3999999999999,
        "id": 571,
        "no_speech_prob": 0.0000042228321035509,
        "seek": 156506,
        "start": 1591.06,
        "temperature": 0,
        "text": " Let's just check b.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1598.98,
        "id": 572,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1595.8999999999999,
        "temperature": 0,
        "text": " And I haven't done any memory cleanup.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1602.8,
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        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1598.98,
        "temperature": 0,
        "text": " So if I say memory.numTensors, is that what it is?",
        "tokens": [
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      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1605.22,
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        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1604.1399999999999,
        "temperature": 0,
        "text": " No, what is it again?",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1607.12,
        "id": 575,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1605.22,
        "temperature": 0,
        "text": " So let's look under memory management.",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1612.48,
        "id": 576,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1609.7,
        "temperature": 0,
        "text": " Memory, oh, memory num, oh, this, tf.",
        "tokens": [
          51096,
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          11,
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          11,
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1614.58,
        "id": 577,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1612.48,
        "temperature": 0,
        "text": " So I know you can't see this, but this is what I want.",
        "tokens": [
          51235,
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1617.6599999999999,
        "id": 578,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1614.58,
        "temperature": 0,
        "text": " I want to check how much, I want to check and see",
        "tokens": [
          51340,
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1620.94,
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        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1617.6599999999999,
        "temperature": 0,
        "text": " like how, if I have cleaned up stuff.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20871982021608215,
        "compression_ratio": 1.7210300429184548,
        "end": 1623.4199999999998,
        "id": 580,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1620.94,
        "temperature": 0,
        "text": " You can see I have 1,147 tensors.",
        "tokens": [
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      },
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        "avg_logprob": -0.20871982021608215,
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        "end": 1624.86,
        "id": 581,
        "no_speech_prob": 0.000018058506611851044,
        "seek": 159506,
        "start": 1623.4199999999998,
        "temperature": 0,
        "text": " So I need to do the memory cleanup.",
        "tokens": [
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      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1626.54,
        "id": 582,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1625.6599999999999,
        "temperature": 0,
        "text": " I don't know, probably better practice would be",
        "tokens": [
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      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1627.84,
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        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1626.54,
        "temperature": 0,
        "text": " for me to clean up as I'm going,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1630.4199999999998,
        "id": 584,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1627.84,
        "temperature": 0,
        "text": " but I'm kind of going to clean up at the end.",
        "tokens": [
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      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1633.4599999999998,
        "id": 585,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1630.4199999999998,
        "temperature": 0,
        "text": " All right, so let's, I just want to click back here",
        "tokens": [
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          11,
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1635.62,
        "id": 586,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1633.4599999999998,
        "temperature": 0,
        "text": " for a second, oops, no.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1638.6999999999998,
        "id": 587,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1635.62,
        "temperature": 0,
        "text": " I'm just going to click no loop to shut this off.",
        "tokens": [
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      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1639.54,
        "id": 588,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1638.6999999999998,
        "temperature": 0,
        "text": " And let's go here.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1640.4199999999998,
        "id": 589,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1639.54,
        "temperature": 0,
        "text": " So what do I need to do?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1642.56,
        "id": 590,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1640.4199999999998,
        "temperature": 0,
        "text": " Ah, I need to visualize what's going on.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1645.5,
        "id": 591,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1643.56,
        "temperature": 0,
        "text": " All right, so how do I do that?",
        "tokens": [
          51299,
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1648.1599999999999,
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        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1645.5,
        "temperature": 0,
        "text": " So I need to draw a line.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1652.1599999999999,
        "id": 593,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1649.32,
        "temperature": 0,
        "text": " So the way that I would draw the line is first,",
        "tokens": [
          51587,
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          636,
          300,
          286,
          576,
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      },
      {
        "avg_logprob": -0.20754034519195558,
        "compression_ratio": 1.839080459770115,
        "end": 1653.8799999999999,
        "id": 594,
        "no_speech_prob": 0.0000027694040909409523,
        "seek": 162486,
        "start": 1652.1599999999999,
        "temperature": 0,
        "text": " what I would do is all I really need",
        "tokens": [
          51729,
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          360,
          307,
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1657.2,
        "id": 595,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1653.88,
        "temperature": 0,
        "text": " is to give myself the X value of zero",
        "tokens": [
          50364,
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1660.6000000000001,
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        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1657.2,
        "temperature": 0,
        "text": " and the X value of one, get the two Y values",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1662.94,
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        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1660.6000000000001,
        "temperature": 0,
        "text": " and draw a line between those two points.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1667.94,
        "id": 598,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1662.94,
        "temperature": 0,
        "text": " So if I were to say, let X equal TF scalar.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1675.1000000000001,
        "id": 599,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1671.22,
        "temperature": 0,
        "text": " This is silly for me to use the predict function.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1675.94,
        "id": 600,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1675.1000000000001,
        "temperature": 0,
        "text": " Why not?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1676.7600000000002,
        "id": 601,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1675.94,
        "temperature": 0,
        "text": " Why not?",
        "tokens": [
          51467,
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1678.0400000000002,
        "id": 602,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1676.7600000000002,
        "temperature": 0,
        "text": " Let's use the predict function.",
        "tokens": [
          51508,
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1680.16,
        "id": 603,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1678.0400000000002,
        "temperature": 0,
        "text": " TF scalar zero.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23580554924388925,
        "compression_ratio": 1.7049180327868851,
        "end": 1682.88,
        "id": 604,
        "no_speech_prob": 8.579232257943659e-7,
        "seek": 165388,
        "start": 1680.16,
        "temperature": 0,
        "text": " So X one is TF scalar zero.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1688.0400000000002,
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        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1683.0400000000002,
        "temperature": 0,
        "text": " Y one equals TF equals predict X one.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1693.9,
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        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1690.68,
        "temperature": 0,
        "text": " X two equals TF scalar one.",
        "tokens": [
          50754,
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1698.72,
        "id": 607,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1694.8400000000001,
        "temperature": 0,
        "text": " Y two equals predict X two.",
        "tokens": [
          50962,
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1700.1200000000001,
        "id": 608,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1698.72,
        "temperature": 0,
        "text": " Right, so this should give me,",
        "tokens": [
          51156,
          1779,
          11,
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          341,
          820,
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          11,
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1702.7600000000002,
        "id": 609,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1700.1200000000001,
        "temperature": 0,
        "text": " I mean, it's a little bit silly for me to not just do this,",
        "tokens": [
          51226,
          286,
          914,
          11,
          309,
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          257,
          707,
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1706,
        "id": 610,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1702.7600000000002,
        "temperature": 0,
        "text": " keep an extra copy of like M and B as regular numbers,",
        "tokens": [
          51358,
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          364,
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          295,
          411,
          376,
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      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1707.5200000000002,
        "id": 611,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1706,
        "temperature": 0,
        "text": " but let's keep going with this.",
        "tokens": [
          51520,
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        ]
      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1708.8200000000002,
        "id": 612,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1707.5200000000002,
        "temperature": 0,
        "text": " Will this work?",
        "tokens": [
          51596,
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        ]
      },
      {
        "avg_logprob": -0.20810660575200052,
        "compression_ratio": 1.5497630331753554,
        "end": 1711.4,
        "id": 613,
        "no_speech_prob": 0.0000019638039248093264,
        "seek": 168288,
        "start": 1708.8200000000002,
        "temperature": 0,
        "text": " Is it going to be able to take a scalar",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1712.76,
        "id": 614,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1711.4,
        "temperature": 0,
        "text": " and make a 1D tensor?",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1714.1200000000001,
        "id": 615,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1712.76,
        "temperature": 0,
        "text": " I think so.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1716.2,
        "id": 616,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1714.1200000000001,
        "temperature": 0,
        "text": " So let me just see here.",
        "tokens": [
          50500,
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        ]
      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1721.1200000000001,
        "id": 617,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1716.2,
        "temperature": 0,
        "text": " So let me do X one dot print, Y one dot print.",
        "tokens": [
          50604,
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          385,
          360,
          1783,
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          5893,
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      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1722.2800000000002,
        "id": 618,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1721.1200000000001,
        "temperature": 0,
        "text": " So let's see that.",
        "tokens": [
          50850,
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          536,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1726.98,
        "id": 619,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1723.92,
        "temperature": 0,
        "text": " Tensor 1D requires values to be a flat typed array.",
        "tokens": [
          50990,
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      },
      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1729.5600000000002,
        "id": 620,
        "no_speech_prob": 0.0000054222177823248785,
        "seek": 171140,
        "start": 1728.5600000000002,
        "temperature": 0,
        "text": " All right, so one thing I could do",
        "tokens": [
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      },
      {
        "avg_logprob": -0.21366976153466008,
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        "seek": 171140,
        "start": 1729.5600000000002,
        "temperature": 0,
        "text": " is instead of making it a scalar,",
        "tokens": [
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      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
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        "temperature": 0,
        "text": " I can make it a 1D tensor.",
        "tokens": [
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      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
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        "text": " That's what it wants and do the same thing here.",
        "tokens": [
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      {
        "avg_logprob": -0.21366976153466008,
        "compression_ratio": 1.6575342465753424,
        "end": 1740.8000000000002,
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        "seek": 171140,
        "start": 1738.96,
        "temperature": 0,
        "text": " And I have to put it in as an array then,",
        "tokens": [
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        "avg_logprob": -0.2284960866976185,
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        "end": 1742.12,
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        "seek": 174080,
        "start": 1741.2,
        "temperature": 0,
        "text": " but it's just one value.",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
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        "temperature": 0,
        "text": " Oh, this is so silly.",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
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        "seek": 174080,
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        "temperature": 0,
        "text": " Why am I doing X one and one?",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1748.52,
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        "text": " I could just do this, right?",
        "tokens": [
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        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1751.76,
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        "seek": 174080,
        "start": 1748.52,
        "temperature": 0,
        "text": " Xs, once again, can I use Xs?",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1753.32,
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        "seek": 174080,
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        "text": " Yeah, yeah, yeah.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1756.8,
        "id": 631,
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        "temperature": 0,
        "text": " So I could just do it with zero and one.",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
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        "seek": 174080,
        "start": 1756.8,
        "temperature": 0,
        "text": " Constant Xs and then constant Ys equals predict Xs.",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1765.54,
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        "temperature": 0,
        "text": " Right, so I could have both these points now.",
        "tokens": [
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      {
        "avg_logprob": -0.2284960866976185,
        "compression_ratio": 1.580188679245283,
        "end": 1770.54,
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        "no_speech_prob": 0.0000017061830703823944,
        "seek": 174080,
        "start": 1765.54,
        "temperature": 0,
        "text": " Then let's say Xs dot print, Ys dot print.",
        "tokens": [
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        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1772.48,
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        "seek": 177080,
        "start": 1771.6399999999999,
        "temperature": 0,
        "text": " Let's look at that.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1773.68,
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        "no_speech_prob": 0.00002078516445180867,
        "seek": 177080,
        "start": 1772.48,
        "temperature": 0,
        "text": " Let's see if this works.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1779.12,
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        "seek": 177080,
        "start": 1776.2,
        "temperature": 0,
        "text": " Predict is not defined because my E key doesn't work",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1780.96,
        "id": 638,
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        "seek": 177080,
        "start": 1779.12,
        "temperature": 0,
        "text": " and I have to type it several times.",
        "tokens": [
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      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1787,
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        "seek": 177080,
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        "temperature": 0,
        "text": " Tensor 1D requires values to be a flat typed array.",
        "tokens": [
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      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1790.72,
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        "seek": 177080,
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        "temperature": 0,
        "text": " Oh, silly, silly me.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2449091967414407,
        "compression_ratio": 1.4685714285714286,
        "end": 1792.6399999999999,
        "id": 641,
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        "seek": 177080,
        "start": 1790.72,
        "temperature": 0,
        "text": " Predict doesn't want a tensor.",
        "tokens": [
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        "end": 1794.56,
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        "temperature": 0,
        "text": " Oh, it wants this.",
        "tokens": [
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      },
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        "avg_logprob": -0.29554858960603414,
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        "temperature": 0,
        "text": " So line X equal, let me just,",
        "tokens": [
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        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1806.72,
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        "seek": 179456,
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        "temperature": 0,
        "text": " this is a little bit silly, but I'm gonna do this.",
        "tokens": [
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        "avg_logprob": -0.29554858960603414,
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        "end": 1809.2,
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        "temperature": 0,
        "text": " So I'm gonna make the, oh,",
        "tokens": [
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      {
        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1811.6,
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        "seek": 179456,
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        "temperature": 0,
        "text": " oh, but I don't need to know the Xs.",
        "tokens": [
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      {
        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1815.2,
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        "seek": 179456,
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        "temperature": 0,
        "text": " I don't need to have the Xs as a tensor because, yeah.",
        "tokens": [
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      {
        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1817.84,
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        "no_speech_prob": 0.00005144220631336793,
        "seek": 179456,
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        "temperature": 0,
        "text": " Sorry, everybody.",
        "tokens": [
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      {
        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1820.5,
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        "no_speech_prob": 0.00005144220631336793,
        "seek": 179456,
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        "temperature": 0,
        "text": " There we go.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.29554858960603414,
        "compression_ratio": 1.5027932960893855,
        "end": 1823.8,
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        "seek": 179456,
        "start": 1820.5,
        "temperature": 0,
        "text": " My predict function, I totally forgot.",
        "tokens": [
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        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
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        "seek": 182380,
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        "temperature": 0,
        "text": " See, this is just,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1826.6399999999999,
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        "temperature": 0,
        "text": " there's so many different ways you could do this.",
        "tokens": [
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      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
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        "no_speech_prob": 0.00004198628448648378,
        "seek": 182380,
        "start": 1826.6399999999999,
        "temperature": 0,
        "text": " Like I could enforce you to convert to a tensor",
        "tokens": [
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      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1831.08,
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        "seek": 182380,
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        "temperature": 0,
        "text": " before you pass it into predict,",
        "tokens": [
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        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1834.52,
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        "no_speech_prob": 0.00004198628448648378,
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        "temperature": 0,
        "text": " but a lot of these decisions are completely arbitrary.",
        "tokens": [
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      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
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        "seek": 182380,
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        "temperature": 0,
        "text": " So you might have a better way of doing it,",
        "tokens": [
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      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1837.76,
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        "no_speech_prob": 0.00004198628448648378,
        "seek": 182380,
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        "temperature": 0,
        "text": " but still I'm gonna do this.",
        "tokens": [
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        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1840.44,
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        "no_speech_prob": 0.00004198628448648378,
        "seek": 182380,
        "start": 1837.76,
        "temperature": 0,
        "text": " So now I have the Xs and the Ys,",
        "tokens": [
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      {
        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1842.46,
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        "no_speech_prob": 0.00004198628448648378,
        "seek": 182380,
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        "temperature": 0,
        "text": " and I don't even need to say Xs print.",
        "tokens": [
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        "avg_logprob": -0.23438641703720633,
        "compression_ratio": 1.6334519572953736,
        "end": 1844.8799999999999,
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        "no_speech_prob": 0.00004198628448648378,
        "seek": 182380,
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        "temperature": 0,
        "text": " So we can see, okay, great.",
        "tokens": [
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        "temperature": 0,
        "text": " So I'm getting these points.",
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        "text": " Kate Wieckmann in the chat makes an excellent point,",
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        "start": 1851.44,
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        "text": " which is that I should think about actually mapping it",
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        "text": " between negative one and one with zero, zero in the center.",
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        "text": " That's not such a bad idea.",
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        "text": " Let me just keep going with this,",
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        "text": " and then maybe I'll change that after the fact,",
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        "text": " because this should work anyway.",
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        "text": " So now here's the thing.",
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        "end": 1870.9,
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        "temperature": 0,
        "text": " Here's the awkward thing.",
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        "temperature": 0,
        "text": " In order for me, all I need to do now",
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        "text": " is basically say this.",
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        "seek": 185144,
        "start": 1877.3200000000002,
        "temperature": 0,
        "text": " Let X1 equal map Xs zero,",
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        "text": " which goes between zero and one,",
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        "text": " one between zero and with.",
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        "end": 1885.2,
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        "text": " And this is kind of silly",
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        "text": " because I could just multiply it times with.",
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        "text": " But I'm gonna just go with the normalizing,",
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        "text": " the full normalizing.",
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        "temperature": 0,
        "text": " Y1 equals map X, oh, sorry, X2,",
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        "text": " which map Xs index one.",
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        "temperature": 0,
        "text": " So this gives me X1, which is just zero and with.",
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        "temperature": 0,
        "text": " Now, Y1 equals map Xs index one.",
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        "temperature": 0,
        "text": " And Y2, I wanna map Ys,",
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        "text": " the Y value is between height and zero,",
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        "text": " because I'm flipping it.",
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        "text": " The problem is, and then I just wanna say line,",
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        "seek": 191080,
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        "temperature": 0,
        "text": " X1, Y1, X2, Y2.",
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        "temperature": 0,
        "text": " So this is really all I need to do.",
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        "text": " I just wanna get the sort of two points on the line",
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        "start": 1943.7199999999998,
        "temperature": 0,
        "text": " and then draw a line between them.",
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        "temperature": 0,
        "text": " This is fine because my Xs are not tensors.",
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        "temperature": 0,
        "text": " And I can use plain numbers right here, X1, X2.",
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        "end": 1957.7399999999998,
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        "seek": 193912,
        "start": 1954.02,
        "temperature": 0,
        "text": " But my Ys, and here, but my Ys are tensors.",
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        "end": 1959.52,
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        "temperature": 0,
        "text": " So for me to be able to,",
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        "end": 1961.6399999999999,
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        "temperature": 0,
        "text": " I really need to get the values back.",
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        "end": 1966.1999999999998,
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        "start": 1961.6399999999999,
        "temperature": 0,
        "text": " And a way to do that is with the function called data.",
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        "end": 1972.16,
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        "seek": 196620,
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        "temperature": 0,
        "text": " So I'm gonna say, let line Y equal Ys.data.",
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        "end": 1979.72,
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        "temperature": 0,
        "text": " And I'm just gonna say data sync right now.",
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        "end": 1981.64,
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        "start": 1979.72,
        "temperature": 0,
        "text": " And let me comment all this out.",
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        "end": 1986.96,
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        "no_speech_prob": 0.0000022603242086915998,
        "seek": 196620,
        "start": 1982.6200000000001,
        "temperature": 0,
        "text": " And let me console log that and see if this comes.",
        "tokens": [
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        "no_speech_prob": 0.0000022603242086915998,
        "seek": 196620,
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        "temperature": 0,
        "text": " So this is kind of a bad idea for a variety of reasons,",
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        "avg_logprob": -0.2231709389459519,
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        "seek": 196620,
        "start": 1990.28,
        "temperature": 0,
        "text": " but I think it's gonna work okay.",
        "tokens": [
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        "avg_logprob": -0.23822235734495398,
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        "end": 1996.56,
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        "no_speech_prob": 0.000004425488441484049,
        "seek": 199208,
        "start": 1992.36,
        "temperature": 0,
        "text": " So you can see I'm getting those numbers back",
        "tokens": [
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      {
        "avg_logprob": -0.23822235734495398,
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        "end": 1998.12,
        "id": 705,
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        "seek": 199208,
        "start": 1996.56,
        "temperature": 0,
        "text": " as a float array.",
        "tokens": [
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      {
        "avg_logprob": -0.23822235734495398,
        "compression_ratio": 1.6533333333333333,
        "end": 1999.1999999999998,
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        "seek": 199208,
        "start": 1998.12,
        "temperature": 0,
        "text": " So here's the thing.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.23822235734495398,
        "compression_ratio": 1.6533333333333333,
        "end": 2002.4399999999998,
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        "seek": 199208,
        "start": 1999.1999999999998,
        "temperature": 0,
        "text": " This really requires not a callback, but a promise.",
        "tokens": [
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        "avg_logprob": -0.23822235734495398,
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        "end": 2004.74,
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        "temperature": 0,
        "text": " And I'm so happy I just did a whole video series",
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        "temperature": 0,
        "text": " on promises.",
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        "avg_logprob": -0.23822235734495398,
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        "end": 2008.32,
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        "start": 2005.6,
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        "text": " I really should be saying data.then.",
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        "end": 2011.36,
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        "temperature": 0,
        "text": " And there's even something called tf.nextframe,",
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      {
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        "end": 2012.96,
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        "seek": 199208,
        "start": 2011.36,
        "temperature": 0,
        "text": " which allows me to sort of think about",
        "tokens": [
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        "end": 2015.52,
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        "text": " the asynchronous nature of pulling the data",
        "tokens": [
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      {
        "avg_logprob": -0.23822235734495398,
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        "end": 2017.1599999999999,
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        "seek": 199208,
        "start": 2015.52,
        "temperature": 0,
        "text": " out of a tensor into a number",
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      },
      {
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        "end": 2018.6999999999998,
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        "start": 2017.1599999999999,
        "temperature": 0,
        "text": " that I can use in an animation.",
        "tokens": [
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        "end": 2019.76,
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        "temperature": 0,
        "text": " These are key things,",
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      },
      {
        "avg_logprob": -0.23822235734495398,
        "compression_ratio": 1.6533333333333333,
        "end": 2021.54,
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        "temperature": 0,
        "text": " and I'm definitely gonna have to get to them.",
        "tokens": [
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      {
        "avg_logprob": -0.24406411912706164,
        "compression_ratio": 1.592885375494071,
        "end": 2023.22,
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        "no_speech_prob": 0.0000030415963010455016,
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        "temperature": 0,
        "text": " But here's the thing.",
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      },
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        "avg_logprob": -0.24406411912706164,
        "compression_ratio": 1.592885375494071,
        "end": 2024.18,
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        "no_speech_prob": 0.0000030415963010455016,
        "seek": 202154,
        "start": 2023.22,
        "temperature": 0,
        "text": " This is just two numbers.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.24406411912706164,
        "compression_ratio": 1.592885375494071,
        "end": 2027.74,
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        "no_speech_prob": 0.0000030415963010455016,
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        "text": " I think my animation can handle using data sync.",
        "tokens": [
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      {
        "avg_logprob": -0.24406411912706164,
        "compression_ratio": 1.592885375494071,
        "end": 2030.98,
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        "temperature": 0,
        "text": " And maybe somebody from the TensorFlow.js team",
        "tokens": [
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      {
        "avg_logprob": -0.24406411912706164,
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        "end": 2034.06,
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        "text": " is gonna wanna say, actually, this is not just a bad idea,",
        "tokens": [
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      {
        "avg_logprob": -0.24406411912706164,
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        "end": 2035.98,
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        "text": " but a really bad idea, I'm not so sure.",
        "tokens": [
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      {
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        "temperature": 0,
        "text": " But I think it's gonna, let's just get it to work",
        "tokens": [
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        "end": 2039.58,
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        "temperature": 0,
        "text": " and see if this demonstrates the idea.",
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        "temperature": 0,
        "text": " So now, and I'm gonna call this line Y,",
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        "temperature": 0,
        "text": " I should be able to say Y1, Y2,",
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        "end": 2051.48,
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        "start": 2047.74,
        "temperature": 0,
        "text": " and I should get zero and one from line Y.",
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      {
        "avg_logprob": -0.280200127994313,
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        "end": 2057.06,
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        "seek": 204774,
        "start": 2053.06,
        "temperature": 0,
        "text": " And now, we should see, we should be done.",
        "tokens": [
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      {
        "avg_logprob": -0.280200127994313,
        "compression_ratio": 1.387878787878788,
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        "seek": 204774,
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        "temperature": 0,
        "text": " Oh, line Y is not defined.",
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      {
        "avg_logprob": -0.280200127994313,
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        "end": 2063.4,
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        "seek": 204774,
        "start": 2062.56,
        "temperature": 0,
        "text": " Where?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.280200127994313,
        "compression_ratio": 1.387878787878788,
        "end": 2066.4,
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        "seek": 204774,
        "start": 2064.38,
        "temperature": 0,
        "text": " Sketch.js line 61.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.280200127994313,
        "compression_ratio": 1.387878787878788,
        "end": 2069.08,
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        "seek": 204774,
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        "temperature": 0,
        "text": " I think I just didn't hit save.",
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        "temperature": 0,
        "text": " Yeah.",
        "tokens": [
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      },
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        "avg_logprob": -0.280200127994313,
        "compression_ratio": 1.387878787878788,
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        "temperature": 0,
        "text": " Oh, I haven't clicked any points.",
        "tokens": [
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        "avg_logprob": -0.280200127994313,
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        "end": 2075.94,
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        "no_speech_prob": 0.000014285512406786438,
        "seek": 204774,
        "start": 2075.06,
        "temperature": 0,
        "text": " Hey, look at that.",
        "tokens": [
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      },
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        "end": 2078.82,
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        "no_speech_prob": 0.0000028573153940669727,
        "seek": 207594,
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        "temperature": 0,
        "text": " Oh, hey, look, it's working.",
        "tokens": [
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        "avg_logprob": -0.31270721855513545,
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        "seek": 207594,
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        "temperature": 0,
        "text": " Oh, that's so exciting.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31270721855513545,
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        "end": 2081.58,
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        "seek": 207594,
        "start": 2080.7400000000002,
        "temperature": 0,
        "text": " Ding.",
        "tokens": [
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        ]
      },
      {
        "avg_logprob": -0.31270721855513545,
        "compression_ratio": 1.592964824120603,
        "end": 2082.98,
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        "temperature": 0,
        "text": " All right, for a couple things.",
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        "seek": 207594,
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        "temperature": 0,
        "text": " Number one is, let's say stroke weight two.",
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        "id": 742,
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        "seek": 207594,
        "start": 2090.06,
        "temperature": 0,
        "text": " And by the way, we can now start to play",
        "tokens": [
          51070,
          400,
          538,
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          636,
          11,
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      {
        "avg_logprob": -0.31270721855513545,
        "compression_ratio": 1.592964824120603,
        "end": 2092.66,
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        "no_speech_prob": 0.0000028573153940669727,
        "seek": 207594,
        "start": 2091.7400000000002,
        "temperature": 0,
        "text": " with the learning rate.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.31270721855513545,
        "compression_ratio": 1.592964824120603,
        "end": 2094.86,
        "id": 744,
        "no_speech_prob": 0.0000028573153940669727,
        "seek": 207594,
        "start": 2092.66,
        "temperature": 0,
        "text": " I don't have to clean up the memory stuff.",
        "tokens": [
          51200,
          286,
          500,
          380,
          362,
          281,
          2541,
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          264,
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      },
      {
        "avg_logprob": -0.31270721855513545,
        "compression_ratio": 1.592964824120603,
        "end": 2098.38,
        "id": 745,
        "no_speech_prob": 0.0000028573153940669727,
        "seek": 207594,
        "start": 2096.26,
        "temperature": 0,
        "text": " I have to, we can play with the learning rate.",
        "tokens": [
          51380,
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          11,
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      },
      {
        "avg_logprob": -0.31270721855513545,
        "compression_ratio": 1.592964824120603,
        "end": 2100.98,
        "id": 746,
        "no_speech_prob": 0.0000028573153940669727,
        "seek": 207594,
        "start": 2098.38,
        "temperature": 0,
        "text": " Like, let's make this 0.01.",
        "tokens": [
          51486,
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          11,
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      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2106.46,
        "id": 747,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2101.46,
        "temperature": 0,
        "text": " So you can see what happens with this lower learning rate.",
        "tokens": [
          50388,
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          393,
          536,
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          365,
          341,
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      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2108.3,
        "id": 748,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2107.18,
        "temperature": 0,
        "text": " I don't know if it's...",
        "tokens": [
          50674,
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          500,
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          498,
          309,
          311,
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      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2113.7,
        "id": 749,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2112.46,
        "temperature": 0,
        "text": " Let's see, is it really working?",
        "tokens": [
          50938,
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          536,
          11,
          307,
          309,
          534,
          1364,
          30,
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      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2115.44,
        "id": 750,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2113.7,
        "temperature": 0,
        "text": " Well, I shouldn't use such a low learning rate.",
        "tokens": [
          51000,
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          11,
          286,
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          380,
          764,
          1270,
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      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2116.5,
        "id": 751,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2115.44,
        "temperature": 0,
        "text": " Let's make it 0.5.",
        "tokens": [
          51087,
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          311,
          652,
          309,
          1958,
          13,
          20,
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      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2121.98,
        "id": 752,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2118.9,
        "temperature": 0,
        "text": " Yeah, it's definitely happy.",
        "tokens": [
          51260,
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          11,
          309,
          311,
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          13,
          51414
        ]
      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2123.58,
        "id": 753,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2121.98,
        "temperature": 0,
        "text": " Okay, so this is working.",
        "tokens": [
          51414,
          1033,
          11,
          370,
          341,
          307,
          1364,
          13,
          51494
        ]
      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2125.26,
        "id": 754,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2123.58,
        "temperature": 0,
        "text": " Linear regression with gradient descent.",
        "tokens": [
          51494,
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          289,
          24590,
          365,
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          13,
          51578
        ]
      },
      {
        "avg_logprob": -0.26411532372543495,
        "compression_ratio": 1.4759615384615385,
        "end": 2126.92,
        "id": 755,
        "no_speech_prob": 0.00001618757778487634,
        "seek": 210098,
        "start": 2125.26,
        "temperature": 0,
        "text": " But I have a severe problem.",
        "tokens": [
          51578,
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          362,
          257,
          8922,
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          13,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2132.04,
        "id": 756,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2127.76,
        "temperature": 0,
        "text": " I am just filling the GPU with tensors and tensors",
        "tokens": [
          50406,
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          264,
          18407,
          365,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2135.48,
        "id": 757,
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        "seek": 212692,
        "start": 2132.04,
        "temperature": 0,
        "text": " and tensors and tensors and never cleaning them up.",
        "tokens": [
          50620,
          293,
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          830,
          293,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2137.84,
        "id": 758,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2135.48,
        "temperature": 0,
        "text": " So now it's my job to go through",
        "tokens": [
          50792,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2139.96,
        "id": 759,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2137.84,
        "temperature": 0,
        "text": " and find every place I'm creating a tensor",
        "tokens": [
          50910,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2141.2200000000003,
        "id": 760,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2139.96,
        "temperature": 0,
        "text": " and dispose of it.",
        "tokens": [
          51016,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2143.92,
        "id": 761,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2141.2200000000003,
        "temperature": 0,
        "text": " So I can use tftidy to do that automatically,",
        "tokens": [
          51079,
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          256,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2146.2000000000003,
        "id": 762,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2143.92,
        "temperature": 0,
        "text": " or I can just use the actual dispose function,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2147.88,
        "id": 763,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2146.2000000000003,
        "temperature": 0,
        "text": " which I might be inclined to do at first.",
        "tokens": [
          51328,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2149.32,
        "id": 764,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2147.88,
        "temperature": 0,
        "text": " All right, so let's go through.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2152.76,
        "id": 765,
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        "seek": 212692,
        "start": 2149.32,
        "temperature": 0,
        "text": " So here, these, I do not, I always want to keep them.",
        "tokens": [
          51484,
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          11,
          286,
          360,
          406,
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          286,
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      },
      {
        "avg_logprob": -0.2018570416215537,
        "compression_ratio": 1.7624521072796935,
        "end": 2154.4,
        "id": 766,
        "no_speech_prob": 0.0000044254902604734525,
        "seek": 212692,
        "start": 2152.76,
        "temperature": 0,
        "text": " m and b are variables that I need to keep",
        "tokens": [
          51656,
          275,
          293,
          272,
          366,
          9102,
          300,
          286,
          643,
          281,
          1066,
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2157.14,
        "id": 767,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2154.4,
        "temperature": 0,
        "text": " throughout the course of this program.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2162.6600000000003,
        "id": 768,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2157.98,
        "temperature": 0,
        "text": " Loss, do I just put tidy in here?",
        "tokens": [
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          772,
          11,
          360,
          286,
          445,
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2165.44,
        "id": 769,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2162.6600000000003,
        "temperature": 0,
        "text": " Or should I, let's predict.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2166.96,
        "id": 770,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2165.44,
        "temperature": 0,
        "text": " So do I put tidy in here?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2169.02,
        "id": 771,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2166.96,
        "temperature": 0,
        "text": " Do I wrap tidy here?",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2171.32,
        "id": 772,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2169.02,
        "temperature": 0,
        "text": " What if I just put tidy here?",
        "tokens": [
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          445,
          829,
          34646,
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        ]
      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2174.6800000000003,
        "id": 773,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2171.32,
        "temperature": 0,
        "text": " Like, what if I say tf.tidy",
        "tokens": [
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          11,
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          286,
          584,
          256,
          69,
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2180.2000000000003,
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        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2178.84,
        "temperature": 0,
        "text": " and put all of this?",
        "tokens": [
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          30,
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      },
      {
        "avg_logprob": -0.2571292453342014,
        "compression_ratio": 1.588235294117647,
        "end": 2182.06,
        "id": 775,
        "no_speech_prob": 0.000009223469533026218,
        "seek": 215440,
        "start": 2180.2000000000003,
        "temperature": 0,
        "text": " Will this do it?",
        "tokens": [
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          30,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2185.2599999999998,
        "id": 776,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2182.06,
        "temperature": 0,
        "text": " And then here, I also need to,",
        "tokens": [
          50364,
          400,
          550,
          510,
          11,
          286,
          611,
          643,
          281,
          11,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2189.02,
        "id": 777,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2186.44,
        "temperature": 0,
        "text": " well, here maybe what I'm gonna do is just dispose these.",
        "tokens": [
          50583,
          731,
          11,
          510,
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          437,
          286,
          478,
          799,
          360,
          307,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2190.94,
        "id": 778,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2189.02,
        "temperature": 0,
        "text": " There's no logic to what I'm doing,",
        "tokens": [
          50712,
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          311,
          572,
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          281,
          437,
          286,
          478,
          884,
          11,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2193.2999999999997,
        "id": 779,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2190.94,
        "temperature": 0,
        "text": " but I'm just gonna dispose these manually.",
        "tokens": [
          50808,
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          286,
          478,
          445,
          799,
          42537,
          613,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2198.2999999999997,
        "id": 780,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2193.2999999999997,
        "temperature": 0,
        "text": " Oh, and that's just the y's, right?",
        "tokens": [
          50926,
          876,
          11,
          293,
          300,
          311,
          445,
          264,
          288,
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          11,
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        ]
      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2202.2599999999998,
        "id": 781,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2198.42,
        "temperature": 0,
        "text": " Line y is just, ys is the only thing that's a tensor here.",
        "tokens": [
          51182,
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      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2204.86,
        "id": 782,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2202.2599999999998,
        "temperature": 0,
        "text": " So this should tidy everything,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2208.02,
        "id": 783,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2204.86,
        "temperature": 0,
        "text": " but hopefully not the variables that I need to keep,",
        "tokens": [
          51504,
          457,
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          286,
          643,
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      },
      {
        "avg_logprob": -0.19417423312946902,
        "compression_ratio": 1.7016806722689075,
        "end": 2211.38,
        "id": 784,
        "no_speech_prob": 0.0000012679280416705296,
        "seek": 218206,
        "start": 2208.02,
        "temperature": 0,
        "text": " rather than individually figuring out what to dispose of.",
        "tokens": [
          51662,
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          281,
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      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2215.7000000000003,
        "id": 785,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2211.38,
        "temperature": 0,
        "text": " And down here, I kind of know that this is my only tensor.",
        "tokens": [
          50364,
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          760,
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          11,
          286,
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          295,
          458,
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          341,
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      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2218.86,
        "id": 786,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2215.7000000000003,
        "temperature": 0,
        "text": " This, by the way, I should call this line x,",
        "tokens": [
          50580,
          639,
          11,
          538,
          264,
          636,
          11,
          286,
          820,
          818,
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      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2221.36,
        "id": 787,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2218.86,
        "temperature": 0,
        "text": " just to be consistent with my variable naming.",
        "tokens": [
          50738,
          445,
          281,
          312,
          8398,
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          452,
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      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2229.02,
        "id": 788,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2224.6600000000003,
        "temperature": 0,
        "text": " You know, I'm only using the ys and xs variable name",
        "tokens": [
          51028,
          509,
          458,
          11,
          286,
          478,
          787,
          1228,
          264,
          288,
          82,
          293,
          2031,
          82,
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      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2230.98,
        "id": 789,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2229.02,
        "temperature": 0,
        "text": " when I have something that's actually a tensor,",
        "tokens": [
          51246,
          562,
          286,
          362,
          746,
          300,
          311,
          767,
          257,
          40863,
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      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2233.02,
        "id": 790,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2230.98,
        "temperature": 0,
        "text": " which helps me remember what I need to clean up and not.",
        "tokens": [
          51344,
          597,
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          385,
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          437,
          286,
          643,
          281,
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      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2234.98,
        "id": 791,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2233.02,
        "temperature": 0,
        "text": " Let's see if this goes.",
        "tokens": [
          51446,
          961,
          311,
          536,
          498,
          341,
          1709,
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          51544
        ]
      },
      {
        "avg_logprob": -0.22371217246367553,
        "compression_ratio": 1.536480686695279,
        "end": 2238.08,
        "id": 792,
        "no_speech_prob": 4.11636420949435e-7,
        "seek": 221138,
        "start": 2236.1800000000003,
        "temperature": 0,
        "text": " Okay, it's still running.",
        "tokens": [
          51604,
          1033,
          11,
          309,
          311,
          920,
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2241.84,
        "id": 793,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2239.08,
        "temperature": 0,
        "text": " 221, oh no, okay.",
        "tokens": [
          50414,
          568,
          4436,
          11,
          1954,
          572,
          11,
          1392,
          13,
          50552
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2245.3199999999997,
        "id": 794,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2241.84,
        "temperature": 0,
        "text": " So I'm better, there's fewer tensors,",
        "tokens": [
          50552,
          407,
          286,
          478,
          1101,
          11,
          456,
          311,
          13366,
          10688,
          830,
          11,
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2247.08,
        "id": 795,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2245.3199999999997,
        "temperature": 0,
        "text": " but I haven't cleaned up everything.",
        "tokens": [
          50726,
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          286,
          2378,
          380,
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2248.44,
        "id": 796,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2247.08,
        "temperature": 0,
        "text": " So what could I be missing?",
        "tokens": [
          50814,
          407,
          437,
          727,
          286,
          312,
          5361,
          30,
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        ]
      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2262.42,
        "id": 797,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2257.42,
        "temperature": 0,
        "text": " Maybe the call to predict, wouldn't tidy clean that up?",
        "tokens": [
          51331,
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          264,
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          6069,
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2264.44,
        "id": 798,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2263.04,
        "temperature": 0,
        "text": " All right, I need to debug this somehow.",
        "tokens": [
          51612,
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      },
      {
        "avg_logprob": -0.23410374285226845,
        "compression_ratio": 1.4031413612565444,
        "end": 2267.08,
        "id": 799,
        "no_speech_prob": 0.00000853031815495342,
        "seek": 223808,
        "start": 2264.44,
        "temperature": 0,
        "text": " One thing I could do is start commenting stuff out",
        "tokens": [
          51682,
          1485,
          551,
          286,
          727,
          360,
          307,
          722,
          29590,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2269.2799999999997,
        "id": 800,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2267.08,
        "temperature": 0,
        "text": " to see like where is the memory leak?",
        "tokens": [
          50364,
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          536,
          411,
          689,
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          264,
          4675,
          17143,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2272.36,
        "id": 801,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2269.2799999999997,
        "temperature": 0,
        "text": " So one worry I have, I really think loss and predict,",
        "tokens": [
          50474,
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          472,
          3292,
          286,
          362,
          11,
          286,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2274.92,
        "id": 802,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2272.36,
        "temperature": 0,
        "text": " those functions generate a lot of tensors.",
        "tokens": [
          50628,
          729,
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          8460,
          257,
          688,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2277.7,
        "id": 803,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2274.92,
        "temperature": 0,
        "text": " I believe tftidy should clean up anything,",
        "tokens": [
          50756,
          286,
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          256,
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          820,
          2541,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2282.46,
        "id": 804,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2277.7,
        "temperature": 0,
        "text": " but let's just, for the sake of argument, comment this out.",
        "tokens": [
          50895,
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          445,
          11,
          337,
          264,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2288.58,
        "id": 805,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2284.74,
        "temperature": 0,
        "text": " And now, of course, the learning is no longer happening.",
        "tokens": [
          51247,
          400,
          586,
          11,
          295,
          1164,
          11,
          264,
          2539,
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          13,
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      },
      {
        "avg_logprob": -0.23393769515188118,
        "compression_ratio": 1.518018018018018,
        "end": 2291.04,
        "id": 806,
        "no_speech_prob": 0.0000025215731511707418,
        "seek": 226708,
        "start": 2288.58,
        "temperature": 0,
        "text": " And what I might as well do is console log",
        "tokens": [
          51439,
          400,
          437,
          286,
          1062,
          382,
          731,
          360,
          307,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2297.04,
        "id": 807,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2292.04,
        "temperature": 0,
        "text": " the amount of tensors, not have to like ask for it.",
        "tokens": [
          50414,
          264,
          2372,
          295,
          10688,
          830,
          11,
          406,
          362,
          281,
          411,
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          309,
          13,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2302.04,
        "id": 808,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2298.88,
        "temperature": 0,
        "text": " Whoops, what did I do wrong?",
        "tokens": [
          50756,
          45263,
          11,
          437,
          630,
          286,
          360,
          2085,
          30,
          50914
        ]
      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2304.8,
        "id": 809,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2302.04,
        "temperature": 0,
        "text": " TF memory num tensors, what is it?",
        "tokens": [
          50914,
          40964,
          4675,
          1031,
          10688,
          830,
          11,
          437,
          307,
          309,
          30,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2306.72,
        "id": 810,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2304.8,
        "temperature": 0,
        "text": " How come I can't remember what it is?",
        "tokens": [
          51052,
          1012,
          808,
          286,
          393,
          380,
          1604,
          437,
          309,
          307,
          30,
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        ]
      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2308.12,
        "id": 811,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2306.72,
        "temperature": 0,
        "text": " Num tensors, no.",
        "tokens": [
          51148,
          22592,
          10688,
          830,
          11,
          572,
          13,
          51218
        ]
      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2312.44,
        "id": 812,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2310,
        "temperature": 0,
        "text": " Yes, it's not a function, it's just num tensors.",
        "tokens": [
          51312,
          1079,
          11,
          309,
          311,
          406,
          257,
          2445,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2313.74,
        "id": 813,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2312.44,
        "temperature": 0,
        "text": " Sorry, everybody, okay.",
        "tokens": [
          51434,
          4919,
          11,
          2201,
          11,
          1392,
          13,
          51499
        ]
      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2316.56,
        "id": 814,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2314.6,
        "temperature": 0,
        "text": " So, and I need another parentheses here.",
        "tokens": [
          51542,
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          11,
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          643,
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          510,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2318.52,
        "id": 815,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2316.56,
        "temperature": 0,
        "text": " Ah, little digression there, all right.",
        "tokens": [
          51640,
          2438,
          11,
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      },
      {
        "avg_logprob": -0.2601776753575349,
        "compression_ratio": 1.5851528384279476,
        "end": 2320.88,
        "id": 816,
        "no_speech_prob": 0.00004611273107002489,
        "seek": 229104,
        "start": 2319.48,
        "temperature": 0,
        "text": " All right, so we can see it's growing.",
        "tokens": [
          51786,
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          558,
          11,
          370,
          321,
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          536,
          309,
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        ]
      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2322.6400000000003,
        "id": 817,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2321.6800000000003,
        "temperature": 0,
        "text": " So let's keep commenting stuff out",
        "tokens": [
          50404,
          407,
          718,
          311,
          1066,
          29590,
          1507,
          484,
          50452
        ]
      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2324.84,
        "id": 818,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2322.6400000000003,
        "temperature": 0,
        "text": " to see like what's causing the memory leak.",
        "tokens": [
          50452,
          281,
          536,
          411,
          437,
          311,
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          4675,
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        ]
      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2329.44,
        "id": 819,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2326.12,
        "temperature": 0,
        "text": " Let's comment out this whole area down here.",
        "tokens": [
          50626,
          961,
          311,
          2871,
          484,
          341,
          1379,
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        ]
      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2333.2400000000002,
        "id": 820,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2330.6800000000003,
        "temperature": 0,
        "text": " Ah, good news, everybody.",
        "tokens": [
          50854,
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          11,
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        ]
      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2335.6800000000003,
        "id": 821,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2333.2400000000002,
        "temperature": 0,
        "text": " The memory leak is in that part.",
        "tokens": [
          50982,
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          307,
          294,
          300,
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      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2338.46,
        "id": 822,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2335.6800000000003,
        "temperature": 0,
        "text": " Let's put this back just to be sure.",
        "tokens": [
          51104,
          961,
          311,
          829,
          341,
          646,
          445,
          281,
          312,
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      },
      {
        "avg_logprob": -0.2668749726848838,
        "compression_ratio": 1.6058823529411765,
        "end": 2348,
        "id": 823,
        "no_speech_prob": 0.000002406101657470572,
        "seek": 232088,
        "start": 2344.08,
        "temperature": 0,
        "text": " Okay, ah, so the memory leak is definitely down here.",
        "tokens": [
          51524,
          1033,
          11,
          3716,
          11,
          370,
          264,
          4675,
          17143,
          307,
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      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2351.48,
        "id": 824,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2348,
        "temperature": 0,
        "text": " And I'm probably creating, oh my goodness.",
        "tokens": [
          50364,
          400,
          286,
          478,
          1391,
          4084,
          11,
          1954,
          452,
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      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2355.6,
        "id": 825,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2354.36,
        "temperature": 0,
        "text": " Oh my goodness.",
        "tokens": [
          50682,
          876,
          452,
          8387,
          13,
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        ]
      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2360.16,
        "id": 826,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2359.12,
        "temperature": 0,
        "text": " No, I'm not sure.",
        "tokens": [
          50920,
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          11,
          286,
          478,
          406,
          988,
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        ]
      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2361.84,
        "id": 827,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2360.16,
        "temperature": 0,
        "text": " Well, let's put this back in.",
        "tokens": [
          50972,
          1042,
          11,
          718,
          311,
          829,
          341,
          646,
          294,
          13,
          51056
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      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2364.36,
        "id": 828,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2361.84,
        "temperature": 0,
        "text": " I thought I saw it, but then I didn't again.",
        "tokens": [
          51056,
          286,
          1194,
          286,
          1866,
          309,
          11,
          457,
          550,
          286,
          994,
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          51182
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      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2367.72,
        "id": 829,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2364.36,
        "temperature": 0,
        "text": " So this is a tensor and I'm disposing it.",
        "tokens": [
          51182,
          407,
          341,
          307,
          257,
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          293,
          286,
          478,
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      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2373.68,
        "id": 830,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2370.4,
        "temperature": 0,
        "text": " Oh, predict, aha.",
        "tokens": [
          51484,
          876,
          11,
          6069,
          11,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.2185643733232871,
        "compression_ratio": 1.4540229885057472,
        "end": 2376.56,
        "id": 831,
        "no_speech_prob": 0.0000132119585032342,
        "seek": 234800,
        "start": 2373.68,
        "temperature": 0,
        "text": " The predict function makes other tensors.",
        "tokens": [
          51648,
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          6069,
          2445,
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          661,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2379.54,
        "id": 832,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2376.56,
        "temperature": 0,
        "text": " And predict got cleaned up because it was in tidy,",
        "tokens": [
          50364,
          400,
          6069,
          658,
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          493,
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          309,
          390,
          294,
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      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2383.2599999999998,
        "id": 833,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2379.54,
        "temperature": 0,
        "text": " but I'm just manually disposing the y's down there.",
        "tokens": [
          50513,
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          478,
          445,
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      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2384.4,
        "id": 834,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2383.2599999999998,
        "temperature": 0,
        "text": " That's what it is.",
        "tokens": [
          50699,
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        ]
      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2386.2,
        "id": 835,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2384.4,
        "temperature": 0,
        "text": " So let me use tidy.",
        "tokens": [
          50756,
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          385,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2391.4,
        "id": 836,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2389,
        "temperature": 0,
        "text": " I guess, ah.",
        "tokens": [
          50986,
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      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2393.64,
        "id": 837,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2392.44,
        "temperature": 0,
        "text": " So let me do this.",
        "tokens": [
          51158,
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        ]
      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2395.2799999999997,
        "id": 838,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2393.64,
        "temperature": 0,
        "text": " Let me put this up here.",
        "tokens": [
          51218,
          961,
          385,
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          341,
          493,
          510,
          13,
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        ]
      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2399.48,
        "id": 839,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2395.2799999999997,
        "temperature": 0,
        "text": " So this is really what I need to tidy.",
        "tokens": [
          51300,
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          286,
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          13,
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      },
      {
        "avg_logprob": -0.24302044543591175,
        "compression_ratio": 1.5664739884393064,
        "end": 2404.36,
        "id": 840,
        "no_speech_prob": 0.0000167014204635052,
        "seek": 237656,
        "start": 2401.84,
        "temperature": 0,
        "text": " So instead of disposing manually,",
        "tokens": [
          51628,
          407,
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          295,
          4920,
          6110,
          16945,
          11,
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        ]
      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2407.8,
        "id": 841,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2405.36,
        "temperature": 0,
        "text": " I kind of like disposing things manually.",
        "tokens": [
          50414,
          286,
          733,
          295,
          411,
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          6110,
          721,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2409.96,
        "id": 842,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2407.8,
        "temperature": 0,
        "text": " The tidy thing kind of freaks me out.",
        "tokens": [
          50536,
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          551,
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          295,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2413.36,
        "id": 843,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2409.96,
        "temperature": 0,
        "text": " But the problem with this is I have a scope issue,",
        "tokens": [
          50644,
          583,
          264,
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          365,
          341,
          307,
          286,
          362,
          257,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2417.34,
        "id": 844,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2413.36,
        "temperature": 0,
        "text": " which is that line y, right?",
        "tokens": [
          50814,
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          300,
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          11,
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        ]
      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2419.42,
        "id": 845,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2417.34,
        "temperature": 0,
        "text": " No matter what I do, if I take this out here,",
        "tokens": [
          51013,
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          1871,
          437,
          286,
          360,
          11,
          498,
          286,
          747,
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          11,
          51117
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2421.36,
        "id": 846,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2419.42,
        "temperature": 0,
        "text": " like this is gonna tidy everything.",
        "tokens": [
          51117,
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          307,
          799,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2423.88,
        "id": 847,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2421.36,
        "temperature": 0,
        "text": " So I guess it's not the biggest deal,",
        "tokens": [
          51214,
          407,
          286,
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          309,
          311,
          406,
          264,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2424.96,
        "id": 848,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2423.88,
        "temperature": 0,
        "text": " at the moment at least,",
        "tokens": [
          51340,
          412,
          264,
          1623,
          412,
          1935,
          11,
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        ]
      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2429.6400000000003,
        "id": 849,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2424.96,
        "temperature": 0,
        "text": " for me to just put everything inside the tidy.",
        "tokens": [
          51394,
          337,
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          281,
          445,
          829,
          1203,
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          264,
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      },
      {
        "avg_logprob": -0.23860765980408255,
        "compression_ratio": 1.5897435897435896,
        "end": 2432.1600000000003,
        "id": 850,
        "no_speech_prob": 0.000005014734142605448,
        "seek": 240436,
        "start": 2429.6400000000003,
        "temperature": 0,
        "text": " Well, that's because,",
        "tokens": [
          51628,
          1042,
          11,
          300,
          311,
          570,
          11,
          51754
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2435.3999999999996,
        "id": 851,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2432.16,
        "temperature": 0,
        "text": " let's put everything inside the tidy for right now.",
        "tokens": [
          50364,
          718,
          311,
          829,
          1203,
          1854,
          264,
          34646,
          337,
          558,
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          13,
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        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2437.72,
        "id": 852,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2435.3999999999996,
        "temperature": 0,
        "text": " There's probably a way I could simplify that,",
        "tokens": [
          50526,
          821,
          311,
          1391,
          257,
          636,
          286,
          727,
          20460,
          300,
          11,
          50642
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2438.6,
        "id": 853,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2437.72,
        "temperature": 0,
        "text": " but this should work.",
        "tokens": [
          50642,
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          341,
          820,
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          13,
          50686
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2439.72,
        "id": 854,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2438.6,
        "temperature": 0,
        "text": " Let's give this a try.",
        "tokens": [
          50686,
          961,
          311,
          976,
          341,
          257,
          853,
          13,
          50742
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2444.08,
        "id": 855,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2443.24,
        "temperature": 0,
        "text": " There we go.",
        "tokens": [
          50918,
          821,
          321,
          352,
          13,
          50960
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2447.6,
        "id": 856,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2444.08,
        "temperature": 0,
        "text": " There's only ever five tensors all the time.",
        "tokens": [
          50960,
          821,
          311,
          787,
          1562,
          1732,
          10688,
          830,
          439,
          264,
          565,
          13,
          51136
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2450.52,
        "id": 857,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2447.6,
        "temperature": 0,
        "text": " So there's no more memory leak, five tensors,",
        "tokens": [
          51136,
          407,
          456,
          311,
          572,
          544,
          4675,
          17143,
          11,
          1732,
          10688,
          830,
          11,
          51282
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2452.92,
        "id": 858,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2450.52,
        "temperature": 0,
        "text": " linear regression with gradient descent,",
        "tokens": [
          51282,
          8213,
          24590,
          365,
          16235,
          23475,
          11,
          51402
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2455.44,
        "id": 859,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2452.92,
        "temperature": 0,
        "text": " TensorFlow.js, interactive, here it is.",
        "tokens": [
          51402,
          37624,
          13,
          25530,
          11,
          15141,
          11,
          510,
          309,
          307,
          13,
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        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2457.3999999999996,
        "id": 860,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2455.44,
        "temperature": 0,
        "text": " So what's left here?",
        "tokens": [
          51528,
          407,
          437,
          311,
          1411,
          510,
          30,
          51626
        ]
      },
      {
        "avg_logprob": -0.1614675028570767,
        "compression_ratio": 1.6213991769547325,
        "end": 2460.7599999999998,
        "id": 861,
        "no_speech_prob": 0.0000024824794309097342,
        "seek": 243216,
        "start": 2457.3999999999996,
        "temperature": 0,
        "text": " So number one, things that could be improved.",
        "tokens": [
          51626,
          407,
          1230,
          472,
          11,
          721,
          300,
          727,
          312,
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        ]
      },
      {
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        "text": " So I wanted to talk through some things",
        "tokens": [
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        "end": 2463,
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        "text": " that could be improved,",
        "tokens": [
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      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2465.1200000000003,
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        "seek": 246076,
        "start": 2463,
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        "text": " but already, meiamsamy in the chat",
        "tokens": [
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      {
        "avg_logprob": -0.27077779173851013,
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        "start": 2465.1200000000003,
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        "text": " made a very good suggestion.",
        "tokens": [
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      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2468.44,
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        "seek": 246076,
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        "temperature": 0,
        "text": " This is very awkward how I put everything in tidy.",
        "tokens": [
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        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
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        "text": " So unnecessary.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2472.5200000000004,
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        "start": 2471.0400000000004,
        "temperature": 0,
        "text": " Let me take that out.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2474.9,
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        "seek": 246076,
        "start": 2472.5200000000004,
        "temperature": 0,
        "text": " Because predict returns something,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2478.4,
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        "temperature": 0,
        "text": " I can actually just put the tidy right here.",
        "tokens": [
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      {
        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2480.28,
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        "text": " I don't know why I didn't think of that.",
        "tokens": [
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        "avg_logprob": -0.27077779173851013,
        "compression_ratio": 1.7209302325581395,
        "end": 2481.4,
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        "seek": 246076,
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        "text": " Like I can actually just,",
        "tokens": [
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      },
      {
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        "compression_ratio": 1.7209302325581395,
        "end": 2484.5600000000004,
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        "text": " it's only this predict function that,",
        "tokens": [
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      {
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        "compression_ratio": 1.7209302325581395,
        "end": 2486.6800000000003,
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        "start": 2484.5600000000004,
        "temperature": 0,
        "text": " so I can actually put the tidy right here,",
        "tokens": [
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        "text": " and I can use my fancy ES6 arrow syntax,",
        "tokens": [
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      {
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        "compression_ratio": 1.5679611650485437,
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        "temperature": 0,
        "text": " and the return is now assumed,",
        "tokens": [
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      {
        "avg_logprob": -0.20592479352597837,
        "compression_ratio": 1.5679611650485437,
        "end": 2499.52,
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        "seek": 248668,
        "start": 2496.08,
        "temperature": 0,
        "text": " and then I can just say ys.dispose.",
        "tokens": [
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        "compression_ratio": 1.5679611650485437,
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        "text": " So this should work.",
        "tokens": [
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      },
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        "avg_logprob": -0.20592479352597837,
        "compression_ratio": 1.5679611650485437,
        "end": 2505.08,
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        "seek": 248668,
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        "temperature": 0,
        "text": " Tidy's not going to tidy up this y value,",
        "tokens": [
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      {
        "avg_logprob": -0.20592479352597837,
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        "end": 2508.52,
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        "temperature": 0,
        "text": " but I can dispose that manually once I have the values.",
        "tokens": [
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        "temperature": 0,
        "text": " So I think this will do the trick.",
        "tokens": [
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        "avg_logprob": -0.20592479352597837,
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        "temperature": 0,
        "text": " Let me just take a look at this.",
        "tokens": [
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        "text": " Yeah.",
        "tokens": [
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      {
        "avg_logprob": -0.20592479352597837,
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        "temperature": 0,
        "text": " So this I like better,",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
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        "seek": 251428,
        "start": 2514.28,
        "temperature": 0,
        "text": " and there's probably other styles or ways you could do it.",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2518.36,
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        "seek": 251428,
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        "temperature": 0,
        "text": " The point is you've got to keep track",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2521.44,
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        "start": 2518.36,
        "temperature": 0,
        "text": " of all the tensors you're making and dispose them.",
        "tokens": [
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        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
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        "temperature": 0,
        "text": " Okay, so I think I'm going to wrap this video up.",
        "tokens": [
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        "end": 2527.1600000000003,
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        "text": " I'm getting all these great suggestions from the chat.",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2530.26,
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        "start": 2527.1600000000003,
        "temperature": 0,
        "text": " I could have tidied the tensors here in predict.",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
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        "temperature": 0,
        "text": " So number one is this code is going to get posted",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2536.4,
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        "seek": 251428,
        "start": 2534.2000000000003,
        "temperature": 0,
        "text": " to the Coding Train website in Coding Challenges.",
        "tokens": [
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        "avg_logprob": -0.22871824511054423,
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        "no_speech_prob": 0.000008530316335964017,
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        "temperature": 0,
        "text": " Make your improvements",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2539.6000000000004,
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        "start": 2537.5800000000004,
        "temperature": 0,
        "text": " and add them as community contributions.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2541.5,
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        "seek": 251428,
        "start": 2539.6000000000004,
        "temperature": 0,
        "text": " Some things that I would love to see.",
        "tokens": [
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      {
        "avg_logprob": -0.22871824511054423,
        "compression_ratio": 1.6697819314641744,
        "end": 2543.98,
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        "no_speech_prob": 0.000008530316335964017,
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        "start": 2541.5,
        "temperature": 0,
        "text": " Visualize, graph the loss value.",
        "tokens": [
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        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2545.46,
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        "no_speech_prob": 0.000003288747393526137,
        "seek": 254398,
        "start": 2543.98,
        "temperature": 0,
        "text": " I think there's a way to get the loss.",
        "tokens": [
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      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2547.02,
        "id": 898,
        "no_speech_prob": 0.000003288747393526137,
        "seek": 254398,
        "start": 2545.46,
        "temperature": 0,
        "text": " I'm sure there's a way to get the loss value",
        "tokens": [
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      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2548.06,
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        "temperature": 0,
        "text": " out of that function.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2550.02,
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        "temperature": 0,
        "text": " That's one idea.",
        "tokens": [
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      },
      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2552.32,
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        "temperature": 0,
        "text": " Kay Wieckmann suggested maybe trying",
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        "text": " some of the other optimizers.",
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        "text": " So what happens if I go to the tensorflow.js documentation",
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        "text": " and just use some of these other optimizers?",
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        "text": " What are they?",
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        "text": " What will they do?",
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      {
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        "temperature": 0,
        "text": " Do you get better or worse results?",
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      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
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        "id": 908,
        "no_speech_prob": 0.000003288747393526137,
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        "temperature": 0,
        "text": " Can you make the learning rate somehow interactive?",
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      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2567.66,
        "id": 909,
        "no_speech_prob": 0.000003288747393526137,
        "seek": 254398,
        "start": 2566.22,
        "temperature": 0,
        "text": " Adjust the learning rate?",
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      },
      {
        "avg_logprob": -0.2580657907434412,
        "compression_ratio": 1.7655172413793103,
        "end": 2568.56,
        "id": 910,
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        "temperature": 0,
        "text": " I don't know if you could come up",
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        "avg_logprob": -0.2580657907434412,
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        "text": " with any really clever visual ideas.",
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        "avg_logprob": -0.23717071960022398,
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        "text": " With this.",
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      },
      {
        "avg_logprob": -0.23717071960022398,
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        "text": " But anyway, so but I think I'm good.",
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      {
        "avg_logprob": -0.23717071960022398,
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        "text": " I think I have the basic idea.",
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      {
        "avg_logprob": -0.23717071960022398,
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        "text": " So if you really want to dive as deeply as you can",
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      {
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        "text": " into linear regression with gradient descent,",
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      {
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        "text": " you can go back and watch my other videos",
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      {
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        "text": " where I did this with just JavaScript and p5.js.",
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      {
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        "text": " Now you've seen it with JavaScript, p5.js,",
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      {
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        "text": " and tensorflow.js.",
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        "text": " So I look forward to your feedback",
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      },
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        "text": " and hearing more about it.",
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      },
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        "text": " More tensorflow.js videos to come.",
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        "text": " You know, I want to get to some actual,",
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      {
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        "text": " more practical things that you might want to do",
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        "text": " for interactive creative arts projects.",
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      },
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        "text": " But I'm still in the weeds here",
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        "text": " of just trying to understand the nuts and bolts",
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        "text": " of how the library works.",
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        "text": " So I hope you're enjoying that",
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        "text": " and I look forward to seeing you in future videos.",
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    "transcription": " Hello, you are here watching another coding challenge and this coding challenge, maybe this should just fit and be in one of my tutorial videos, but I'm gonna make it a coding challenge because I'm gonna attempt to do it in one video. And what I'm doing is recreating something that I've done before in some of my machine learning tutorials. And it was suggested here, I don't know if it was suggested exactly, but a Twitter user, Calstube Old Podcar, apologies if I'm pronouncing the name incorrectly, created this interactive simulation of linear regression using TensorFlow.js. And so this is very similar to something that I've done previously, right? I have this video, linear regression with gradient descent, where I just did this with plain JavaScript. And then you could also look at this other video, which I go through the mathematics of gradient descent a little bit. But here's the thing, going through the mathematics, making this video where I implement the mathematics in JavaScript while useful, and perhaps background for this video, one of the exciting things about doing this with TensorFlow.js is TensorFlow.js has a nice API for optimizing loss functions with the gradient descent algorithm built into it. So I could just do things. So let's make a, I gotta come back here, but let's make a list. All right, so first of all, what is linear regression anyway? So let's say we have a space, and I drew this as like a canvas, but really I should be talking just about a generic kind of two-dimensional Cartesian plane. In that plane, there are a bunch of points. The idea of linear regression is to figure out, can we fit, oh, this is a time for another colored marker. Can we fit a line into this two-dimensional space that approximates all of these points as best we can? And I can visually just kind of make myself do this like this. So I can eyeball it and say, this line kind of gets close. What we're trying to do is minimize, I've never, all of these, this is the most beautiful diagram I've ever made, all of these distances of all of the points to the line. The idea here then is that we can make some predictions. If this data, if this x-axis represents height, we might predict on the y-axis weight. You can think of kinds of data sets, simple 2D data sets where there's a linear relationship between the one field of data and another field of data. So if we pick a new height, we can kind of make a guess approximately what that weight is gonna be. That's the idea of linear regression. It's incredibly simple. A lot of data isn't two-dimensional. A lot of data doesn't fit a line. You know, maybe a curve fits it better. And this is more complex scenarios will come as we move forward and make more scenarios with complex polynomial equations or neural network-based learning and other types of machine learning algorithms. But this is a good place for us to start. So what do we need? We need a data set. So we need a set of x's and y's. This is the data set. Right, we need x's and y's. And I'm gonna create that data set through interactive clicking. Interactive clicking is the way I'm gonna create that data set with the mouse. I need to have something called a loss function. The loss function is a way of computing the error. And there are a bunch of different loss functions, and we'll see these as I use TensorFlow.js in more tutorials. I can select different kinds of loss functions for different scenarios. But in this scenario, I'm gonna use a simple basic one, which I believe is called root mean squared error. Did I say that correctly? Is that the right name of it? But the idea is that I wanna look at all of those distances. Okay, I'm back, because I started talking about the loss function, and I realized I really didn't draw. I'm not actually looking for the distance from the point to that line, which would be perpendicular. I'm looking for this vertical distance, which is, so this is what I'm trying to minimize. Right, I'm trying to minimize and get a line that has, and this, that is the least, the sum of all these distances is the smallest number, minimizing the loss. So I have a loss function, I need that. I also need, in TensorFlow.js, something called an optimizer. And the optimizer is the thing that allows me to minimize the loss function. And in order to do that, I also need to have a learning rate. So these are all, I actually missed something very important here, but these are all the pieces. I need the data, I need to define a loss function, I need to define an optimizer. I say, hey, optimizer, minimize the loss function with this learning rate. So keep tweaking the parameters, tweaking the parameters. So that's the thing I forgot. What are those parameters? Well, the formula for a line is y equals mx plus b. M is often referred to as the slope, b as the, I'm back, because I looked it up. M is the slope, and b referred to as the y-intercept. Kind of like bias, by the way, if you've watched some of my other neural network tutorials, because this is like the thing we're doing with all the neurons. Oh, it's all so connected, but we're just living in this very simple place. So I need these parameters, I need these variables, because that's what's going to allow me to create the predictions that are on the line to compare with the actual points, and compute the loss, minimize it, tweaking these values. So tweak these values, minimizing the loss. This is what we're doing. And I've done this before in great detail. This is gonna be in less detail, because TensorFlow.js is gonna do a lot of this for us. The thing that's a little extra complicated is we can't just work with arrays of numbers and variables in the way that we're used to in JavaScript. And so this is what brings me to, if you haven't looked at these particular videos that I've made already, what's a TensorFlow tensor? What's a variable? What's an operation? How's the memory management stuff? This is stuff we're gonna have to lean on while I build this example. And this should be, by the way, an actual practical example of where I need a tf variable. So I kind of, in this video, explained what a tf variable is, and then people just kind of moved on and didn't use it for anything. So hopefully this will show us that. All right, how about we write some code now? So actually, I magically appeared over here for a second, because instead of, I did make a little mistake here. I mean, root mean squared is a perfectly legitimate loss function, but most linear regression with gradient descent examples will not bother with the root. And the root refers to square root. We just want the mean squared error, which means, if I say that this value is y, and this value is the guess, the error is guess minus y and squared. And if I do that for all of these, that's the mean. And average them, it's the mean. So I can really sum them or mean them. Well, it's gonna take care of that for us. So we're just gonna use the mean squared error. But that's the idea. We take the differences. The reason why we have to square it, well, for a variety of reasons. One, it has to do with the derivative stuff that's in my other videos, but also just because positive or negative, it's the distance, the size of the error, whether it's up or down, which is key. All right, so here's the amount of code we're gonna start with. I'm using p5 so that I can draw stuff. I'm making a canvas, and the background is zero, which means it's black, and this is what I have so far. So let's look at our list over here, and let's first add the data set, the Xs and Ys. So this is gonna be easy, because I just wanna have Xs be an array, Ys also be an array. So, and then, whenever I click the mouse, I wanna say, oh, you know what I could do? I could make those vectors, let's make them separate arrays. I think we're gonna, actually, I know we're gonna, we're gonna wanna do that for a variety of reasons. We're gonna keep those as separate arrays. So every time I click the mouse, I'm going to say Xs, push mouse X, Ys, push mouse Y. Ah, okay. Here's a little thing. So this is our canvas, right? This is my drawing of the canvas. I know, now I'm having like a deja vu thing. I totally talked about this in the other video. The width is like 400, the height is 400, but I really wanna think of this as, I really wanna think of zero, zero down here, and maybe one being over here. I wanna normalize everything between zero and one. Everything's just gonna work better if we do that. So with Y pointing up. So I'm gonna do a mapping. So every Y value, that's pixel value between zero and height, I'm gonna map between one and zero, and every X value that's between zero and width, I'm gonna map between zero and one. So let's do that. So I'm going to say, let X equal map mouse X, which goes between zero and width, to between zero and one. Let Y, which is mouse Y, between zero and height, and have that go between one and zero, and then push X and push Y. The other thing I wanna do is I just want to, you know, I'm gonna add a draw loop, and I wanna draw all those points. So I'm also now gonna say, stroke 255, stroke weight four, for let I equal, what, huh? Let I equal zero, I is less than X dot length, I plus plus, and those are actually called Xs, Xs dot length. And what I'm gonna do is I'm gonna say, let pixel X equal map. I really should make just like, I'm probably gonna have to do this a lot, so I should probably make a function that just like normalize and un-normalize or de-normalize. Pix, PX equals map X's index I, which goes between zero and one, back to zero and width. So this is the reverse. PY, which maps Y, which goes between zero and one to height comma zero. And then I wanna say, point PX, PY. So I haven't done any, I haven't even, I'm not even using TensorFlow.js yet. I'm just kind of doing the stuff with P5 to draw things. So let's see if I'm getting the results that I want, which is whenever I click, I get the points there. Perfect. And I kind of wanna see them a bit more. Let's like really make it bigger. Great, that's like too big. Okay, great. So we can see, those are the points I'm clicking on. Okay, so what's next? I need a loss function, I need an optimizer. Ah, oh, I need these. Let's make these. So, cause I'm looking for somewhere where I need to get some TensorFlow.js stuff working. So what I need is I need to have M and B. So let's figure that out. So I'm going to create an M and a B. And I'm not gonna initialize them in setup, up here. And I probably should be using const in various places here to protect myself from reassigning something by accident, but I'm gonna be loosey goosey and just use let. You know, these could be const. But anyway, I'm not gonna get into the whole let versus const thing, it makes me crazy. I'm gonna say up here, M equals TF scalar random one. So I'm gonna use the P5 random function to give me a random number between zero and one, cause I gotta start somewhere. So this is kind of like initializing the weights of a neural network. There is no neural network, I'm just doing, I'm just kind of optimizing this function, Y equals MX plus B, but those are like weights, M and B. So I'm gonna initialize them randomly and scalar cause it's a single number. So go back to my TensorFlow.js intro videos and you'll see. Then B, I'm gonna say the same thing, ah, but, these are the things that have to change, right? The data never changes, it's sort of fixed. M and B change over time. I need to tweak those, which means they have to be variable. They have to be able to change, which means, when I over here, I think what I write is TF variable, I wrap them in the TF variable. So now I have M and B as TF variable, right? Isn't it crazy, like you see this kind of code and you're like, that looks like the craziest, scariest thing, but you realize like it's just like, make a number, and because we're in this like kind of lower level, working on the GPU land, I've gotta be very like specific. Like this is a single number and it's gonna be variable, but really it's just a random number. Now, what do we need to do? We need to write, I don't think I actually said this, but I need to write a function called predict maybe, which takes in all of the Xs, just the Xs, and gives me the Y predictions based on where the line is. Because I need to compare the Y predictions to the actual Y values to get the mean squared error. So let's write that function. I'm putting these in like arbitrary places, but I'm gonna write a function called predict. And there what I need to do is I need to have some Xs and I need to return some Ys. I think that's the idea, right? Yes, so I don't wanna just predict one value, I wanna predict a bunch. So the Xs, here's the thing. So if I call this function, the Xs, if they're just a plain array, I need to make it into a tensor. So I'm gonna call it const TF Xs. That might be a bad, it's tensor. And this is a 1D tensor, tensor 1D. Oh, TF, tensor 1D, I think this will do it, Xs, right? I need to turn it into a tensor. And then I need to have the formula for a line. So I need to say, which is Y equals MX plus B. So what I would be doing is I would be saying TF Xs multiplied, is it MUL or MULT? Multiplied by M plus B. Right, this is the idea. If I'm getting just a plain array of numbers, I turn them into a tensor, then I apply the formula for a line, and these are the predictions, the Ys. I guess, I don't like my naming here. I'm just gonna call this X, and maybe I'll call this Xs, I don't know. I have to think about, I'll come back to this later. Okay, so I have that. Now, let's go back and look at the things that I need. So I have this predict function, I have a data set. Ah, I need a loss function. Need a loss function, and I need a, oh, before we do the loss function, let's create the optimizer and the learning rate. So this is what's wonderful about working with TensorFlow.js. When I say make the optimizer, I just mean make a tf.optimizer. It exists, it'll do this math for us. So let's go to the, this is not something that I covered in my other videos, so let's go look for optimizer, and I want an optimizer. Now, there's all these different kinds of optimizers. SGD, stochastic gradient descent. This means the idea of slowly adjusting M and B to minimize the loss function, and I've covered this in more detail in the other videos. So I'm gonna click on that, and I'm gonna look here, and this is basically what I need to do. All right, we got the code right here. Look, there's even a, look at this. Oh my goodness, there's like some stuff here we can really use. So I'm gonna grab this, and I'm gonna put this up here. So I want a learning rate, and I'm gonna have a much bigger learning rate to start with, and I want an optimizer. So now I have a learning rate and an optimizer, and the optimizer is doing stochastic gradient descent. So I have learning rate, optimizer. Now I need that loss function. I need the loss function. Okay, the loss function is something I'm gonna write, loss, and actually, let's go back to here. So look at this. So this is the fancy ES6 way of writing a function, but I'm gonna write it in a less fancy way, and I'm gonna do this. So what I want is I need the loss function. I have some predictions, and I have some labels. So these are, the predictions are the y values I'm getting from the predict function. The labels are the actual y values that are part of this, and by the way, I'm gonna have to do memory management. Don't worry, if you're screaming at me that I haven't worked about memory management, I'm just gonna do that later. So what I wanna do is say return the predictions minus the labels. That makes sense, right? Because I said here, when I said mean squared error is the predictions, like the guess, minus the labels, which is the actual y squared. And so predictions minus the labels is the predictions squared, and then take the mean of them. Look at this. All of these mathematical operations are inside of TensorFlow.js, and you can chain them. So predictions is a tensor, labels is a tensor. All of these, remember, they're just gonna keep producing new tensors, and I'm gonna have to tidy and clean all this stuff up for memory management, but again, I'll worry about that later. So now I have the loss function. All right, well, what is it that I wanna do? Every time, so let's say, I think I'm actually like, I have everything. I have the loss function, I have the data, I have the optimizer, I have a predict function, I have a learning rate, I can minimize, well, oh, this I haven't done. So the training, the actual training, what does it mean to train it? To train it means minimize the loss function with the optimizer and adjusting M and B based on that. All right, so let's see if we can make that. I have a feeling that was in that page that I went to, so maybe I could just copy it from there. I'm kind of, this is like, it totally is. That's fine, I'm gonna happily cheat here. It was in that example, thank you very much. Thank you, TensorFlow.js documentation. And so I'm gonna just put this in draw. Like, every time through draw, I'm gonna minimize. So let's look at this, oh, look at this. Okay, so this is a little different. So first of all, this is using nice, fancy ES6 arrow notation which I'm somewhat happy about. But let me just write a function here called train. And the idea of the train function is to execute the loss with the predictions and the actual y's. Okay, so here, what I'm really doing is minimizing the train. That's weird, this isn't really, no, training would be doing this. So this is a terrible name for this. And actually, this is silly for me to even name this. It really makes sense for me to just make this an anonymous function and that what I'm minimizing is this. This is what I wanna minimize, the loss function. But if I wanna be nice and ES6-like with my arrow notation, which I think by the fact that I'm using TensorFlow.js and you can watch my arrow notation function if this is, I can kind of get rid of a lot of the extra stuff here and this should be good. So I just wanna minimize the loss function. Now, here's the thing. These have to be tensors, right? The loss function requires predictions and labels. They have to be tensors. And if you remember, my x's and y's aren't tensors. When I call the predict function with the x's, it gives me back a tensor. So that, I can't believe I haven't run this code yet. This is a terrible thing. Usually I try to run my code incrementally all the time. I guess I've forgotten to do that. So probably people in the chat are telling me about mistakes I'm making. So this is a tensor, but this is still a plain array. So what I need to do is say constant and I gotta rethink the naming. Maybe somebody in the chat has an idea for me. I think what I actually should do, I have an idea. Permit me a moment of refactoring. X valves, y valves. So I think when it's not a tensor, I'm just gonna call it like x underscore valves because that's gonna help me remember. So x valves, y valves. And then whenever I say, and this should be x. Whenever I say xs or ys, that's really a tensor. I guess I could have done txs. So here, what I'm doing is predicting from the x valves and then ys is tf.tensor1dy valves. So I need to create that tensor and now I can minimize the loss with predicting from the x valves and the y valves. Okay, so this is good. Let's just run this. All right, I'm from the future, different day, different clothes, I'm breaking into this video to mention something really important that I didn't actually mention when I recorded the coding challenge originally. What is that optimize function doing? How does it actually work? And we need to look at the TensorFlow.js documentation to see, so let me, I'm bringing my laptop back up here and I'm gonna switch over here. So I've got the code from the past, me in the future. This is the part that I'm talking about. Well, how is this going to adjust m and b? Those are the, these are the parameters, the weights, the variables that we need to adjust to minimize that loss function. But I'm not anywhere in here saying, those are the variables to work with. Well, this is part of what TensorFlow.js does natively. The fact that I made these up here, tf variables, means those are variables that can be adjusted. And if we look here at the TensorFlow documentation, you'll see what does minimize do? It executes this function f, that is, sorry, that is this function, right? This whole function here. And by the way, the return here is implicit because I'm using the arrow function. So it minimizes the output of that function, tries to get it lower by computing the gradients with respect to a list of all trainable variables provided by var list. Guess what? I didn't provide a list of trainable variables. If no list is provided, it defaults to all trainable variables. And that's what's going on. That's what I did in this coding challenge. These are all the trainable variables in my system. If I wanted to only use m or only use b, I could put those in a list. So that's all I have to say about that. I'm going to fade now back into the other video, lower this and back up, and it's going to keep going where I debug and have all sorts of other problems. Goodbye. Okay, predict.sub.squared is not a function at loss at optimize or minimize. So what do I have wrong here? In my loss function, sub labels.squared.mean. Oh, you know what it is? There's nothing in the arrays at the beginning. They have zero things in them. So a couple of things, one is I could put something in it, but I think I probably should just say, I shouldn't do it only if x.length is greater than zero. So this is definitely, then do I want to bother with, do I want to bother with doing any of this? If there's no values in there, like calling predict and stuff with an empty array, I think it's going to cause problems. That makes sense. All right, let's try this. X is not defined, x vals, my naming. Okay, sketch 45. Ah, this is x vals. And this is x vals, y vals. Okay, that should be good. All right, let's try this. This is not square, being told in the chat, breaking news, that this is actually.squared, not.squared, is that right? Yeah, oh, it's square, a.square, okay. Okay. There we go, okay. So things are going and there's no, I don't have any, like I could look at, that's m, m, m.print, right? So you can see it's changing. It's actually like training it. Like the value of m is changing. So everything's going and working. The problem is I'm not seeing the results. Let's just check b. And I haven't done any memory cleanup. So if I say memory.numTensors, is that what it is? No, what is it again? So let's look under memory management. Memory, oh, memory num, oh, this, tf. So I know you can't see this, but this is what I want. I want to check how much, I want to check and see like how, if I have cleaned up stuff. You can see I have 1,147 tensors. So I need to do the memory cleanup. I don't know, probably better practice would be for me to clean up as I'm going, but I'm kind of going to clean up at the end. All right, so let's, I just want to click back here for a second, oops, no. I'm just going to click no loop to shut this off. And let's go here. So what do I need to do? Ah, I need to visualize what's going on. All right, so how do I do that? So I need to draw a line. So the way that I would draw the line is first, what I would do is all I really need is to give myself the X value of zero and the X value of one, get the two Y values and draw a line between those two points. So if I were to say, let X equal TF scalar. This is silly for me to use the predict function. Why not? Why not? Let's use the predict function. TF scalar zero. So X one is TF scalar zero. Y one equals TF equals predict X one. X two equals TF scalar one. Y two equals predict X two. Right, so this should give me, I mean, it's a little bit silly for me to not just do this, keep an extra copy of like M and B as regular numbers, but let's keep going with this. Will this work? Is it going to be able to take a scalar and make a 1D tensor? I think so. So let me just see here. So let me do X one dot print, Y one dot print. So let's see that. Tensor 1D requires values to be a flat typed array. All right, so one thing I could do is instead of making it a scalar, I can make it a 1D tensor. That's what it wants and do the same thing here. And I have to put it in as an array then, but it's just one value. Oh, this is so silly. Why am I doing X one and one? I could just do this, right? Xs, once again, can I use Xs? Yeah, yeah, yeah. So I could just do it with zero and one. Constant Xs and then constant Ys equals predict Xs. Right, so I could have both these points now. Then let's say Xs dot print, Ys dot print. Let's look at that. Let's see if this works. Predict is not defined because my E key doesn't work and I have to type it several times. Tensor 1D requires values to be a flat typed array. Oh, silly, silly me. Predict doesn't want a tensor. Oh, it wants this. So line X equal, let me just, this is a little bit silly, but I'm gonna do this. So I'm gonna make the, oh, oh, but I don't need to know the Xs. I don't need to have the Xs as a tensor because, yeah. Sorry, everybody. There we go. My predict function, I totally forgot. See, this is just, there's so many different ways you could do this. Like I could enforce you to convert to a tensor before you pass it into predict, but a lot of these decisions are completely arbitrary. So you might have a better way of doing it, but still I'm gonna do this. So now I have the Xs and the Ys, and I don't even need to say Xs print. So we can see, okay, great. So I'm getting these points. Kate Wieckmann in the chat makes an excellent point, which is that I should think about actually mapping it between negative one and one with zero, zero in the center. That's not such a bad idea. Let me just keep going with this, and then maybe I'll change that after the fact, because this should work anyway. So now here's the thing. Here's the awkward thing. In order for me, all I need to do now is basically say this. Let X1 equal map Xs zero, which goes between zero and one, one between zero and with. And this is kind of silly because I could just multiply it times with. But I'm gonna just go with the normalizing, the full normalizing. Y1 equals map X, oh, sorry, X2, which map Xs index one. So this gives me X1, which is just zero and with. Now, Y1 equals map Xs index one. And Y2, I wanna map Ys, the Y value is between height and zero, because I'm flipping it. The problem is, and then I just wanna say line, X1, Y1, X2, Y2. So this is really all I need to do. I just wanna get the sort of two points on the line and then draw a line between them. This is fine because my Xs are not tensors. And I can use plain numbers right here, X1, X2. But my Ys, and here, but my Ys are tensors. So for me to be able to, I really need to get the values back. And a way to do that is with the function called data. So I'm gonna say, let line Y equal Ys.data. And I'm just gonna say data sync right now. And let me comment all this out. And let me console log that and see if this comes. So this is kind of a bad idea for a variety of reasons, but I think it's gonna work okay. So you can see I'm getting those numbers back as a float array. So here's the thing. This really requires not a callback, but a promise. And I'm so happy I just did a whole video series on promises. I really should be saying data.then. And there's even something called tf.nextframe, which allows me to sort of think about the asynchronous nature of pulling the data out of a tensor into a number that I can use in an animation. These are key things, and I'm definitely gonna have to get to them. But here's the thing. This is just two numbers. I think my animation can handle using data sync. And maybe somebody from the TensorFlow.js team is gonna wanna say, actually, this is not just a bad idea, but a really bad idea, I'm not so sure. But I think it's gonna, let's just get it to work and see if this demonstrates the idea. So now, and I'm gonna call this line Y, I should be able to say Y1, Y2, and I should get zero and one from line Y. And now, we should see, we should be done. Oh, line Y is not defined. Where? Sketch.js line 61. I think I just didn't hit save. Yeah. Oh, I haven't clicked any points. Hey, look at that. Oh, hey, look, it's working. Oh, that's so exciting. Ding. All right, for a couple things. Number one is, let's say stroke weight two. And by the way, we can now start to play with the learning rate. I don't have to clean up the memory stuff. I have to, we can play with the learning rate. Like, let's make this 0.01. So you can see what happens with this lower learning rate. I don't know if it's... Let's see, is it really working? Well, I shouldn't use such a low learning rate. Let's make it 0.5. Yeah, it's definitely happy. Okay, so this is working. Linear regression with gradient descent. But I have a severe problem. I am just filling the GPU with tensors and tensors and tensors and tensors and never cleaning them up. So now it's my job to go through and find every place I'm creating a tensor and dispose of it. So I can use tftidy to do that automatically, or I can just use the actual dispose function, which I might be inclined to do at first. All right, so let's go through. So here, these, I do not, I always want to keep them. m and b are variables that I need to keep throughout the course of this program. Loss, do I just put tidy in here? Or should I, let's predict. So do I put tidy in here? Do I wrap tidy here? What if I just put tidy here? Like, what if I say tf.tidy and put all of this? Will this do it? And then here, I also need to, well, here maybe what I'm gonna do is just dispose these. There's no logic to what I'm doing, but I'm just gonna dispose these manually. Oh, and that's just the y's, right? Line y is just, ys is the only thing that's a tensor here. So this should tidy everything, but hopefully not the variables that I need to keep, rather than individually figuring out what to dispose of. And down here, I kind of know that this is my only tensor. This, by the way, I should call this line x, just to be consistent with my variable naming. You know, I'm only using the ys and xs variable name when I have something that's actually a tensor, which helps me remember what I need to clean up and not. Let's see if this goes. Okay, it's still running. 221, oh no, okay. So I'm better, there's fewer tensors, but I haven't cleaned up everything. So what could I be missing? Maybe the call to predict, wouldn't tidy clean that up? All right, I need to debug this somehow. One thing I could do is start commenting stuff out to see like where is the memory leak? So one worry I have, I really think loss and predict, those functions generate a lot of tensors. I believe tftidy should clean up anything, but let's just, for the sake of argument, comment this out. And now, of course, the learning is no longer happening. And what I might as well do is console log the amount of tensors, not have to like ask for it. Whoops, what did I do wrong? TF memory num tensors, what is it? How come I can't remember what it is? Num tensors, no. Yes, it's not a function, it's just num tensors. Sorry, everybody, okay. So, and I need another parentheses here. Ah, little digression there, all right. All right, so we can see it's growing. So let's keep commenting stuff out to see like what's causing the memory leak. Let's comment out this whole area down here. Ah, good news, everybody. The memory leak is in that part. Let's put this back just to be sure. Okay, ah, so the memory leak is definitely down here. And I'm probably creating, oh my goodness. Oh my goodness. No, I'm not sure. Well, let's put this back in. I thought I saw it, but then I didn't again. So this is a tensor and I'm disposing it. Oh, predict, aha. The predict function makes other tensors. And predict got cleaned up because it was in tidy, but I'm just manually disposing the y's down there. That's what it is. So let me use tidy. I guess, ah. So let me do this. Let me put this up here. So this is really what I need to tidy. So instead of disposing manually, I kind of like disposing things manually. The tidy thing kind of freaks me out. But the problem with this is I have a scope issue, which is that line y, right? No matter what I do, if I take this out here, like this is gonna tidy everything. So I guess it's not the biggest deal, at the moment at least, for me to just put everything inside the tidy. Well, that's because, let's put everything inside the tidy for right now. There's probably a way I could simplify that, but this should work. Let's give this a try. There we go. There's only ever five tensors all the time. So there's no more memory leak, five tensors, linear regression with gradient descent, TensorFlow.js, interactive, here it is. So what's left here? So number one, things that could be improved. So I wanted to talk through some things that could be improved, but already, meiamsamy in the chat made a very good suggestion. This is very awkward how I put everything in tidy. So unnecessary. Let me take that out. Because predict returns something, I can actually just put the tidy right here. I don't know why I didn't think of that. Like I can actually just, it's only this predict function that, so I can actually put the tidy right here, and I can use my fancy ES6 arrow syntax, and the return is now assumed, and then I can just say ys.dispose. So this should work. Tidy's not going to tidy up this y value, but I can dispose that manually once I have the values. So I think this will do the trick. Let me just take a look at this. Yeah. So this I like better, and there's probably other styles or ways you could do it. The point is you've got to keep track of all the tensors you're making and dispose them. Okay, so I think I'm going to wrap this video up. I'm getting all these great suggestions from the chat. I could have tidied the tensors here in predict. So number one is this code is going to get posted to the Coding Train website in Coding Challenges. Make your improvements and add them as community contributions. Some things that I would love to see. Visualize, graph the loss value. I think there's a way to get the loss. I'm sure there's a way to get the loss value out of that function. That's one idea. Kay Wieckmann suggested maybe trying some of the other optimizers. So what happens if I go to the tensorflow.js documentation and just use some of these other optimizers? What are they? What will they do? Do you get better or worse results? Can you make the learning rate somehow interactive? Adjust the learning rate? I don't know if you could come up with any really clever visual ideas. With this. But anyway, so but I think I'm good. I think I have the basic idea. So if you really want to dive as deeply as you can into linear regression with gradient descent, you can go back and watch my other videos where I did this with just JavaScript and p5.js. Now you've seen it with JavaScript, p5.js, and tensorflow.js. So I look forward to your feedback and hearing more about it. More tensorflow.js videos to come. You know, I want to get to some actual, more practical things that you might want to do for interactive creative arts projects. But I'm still in the weeds here of just trying to understand the nuts and bolts of how the library works. So I hope you're enjoying that and I look forward to seeing you in future videos.",
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