https://www.coursera.org/specializations/tensorflow-in-practice?#courses
In this doc you can see my timeline progress.
https://www.coursera.org/learn/introduction-tensorflow
2020-08-22:
- (done) Week 1: Introduction: A conversation with Andrew Ng
- (done) A primer in machine learning
- (done) The ‘Hello World’ of neural networks
- (done) From rules to data
- (done) Working through ‘Hello World’ in TensorFlow and Python
- (done) Try it for yourself
- (done) Week 1 Quiz
- (done) Introduction to Google Colaboratory
- (done) Get started with Google Colaboratory (Coding TensorFlow)
2020-08-23:
- (done): Exercise 1 (Housing Prices)
- (done): Programming Assignment: Exercise 1 (Housing Prices)
- (done): Week 1 Resources
2020-08-24:
- (done): Week 2: A Conversation with Andrew Ng
- (done): An Introduction to computer vision
- (done): Exploring how to use data
- (done): Writing code to load training data
- (done): The structure of Fashion MNIST data
- (done): Coding a Computer Vision Neural Network
- (done): See how it's done
- (done): Walk through a Notebook for computer vision
- (done): Get hands-on with computer vision
- (done): Using Callbacks to control training
- (done): See how to implement Callbacks
- (done): Walk through a notebook with Callbacks
- (done): Week 2 Quiz
- (done): Exercise 2 (Handwriting Recognition)
- (done): Week 2 Resources
- (done): Exercise 2 (Handwriting Recognition)
2020-08-25:
- (done): Week 3: A conversation with Andrew Ng
- (done): What are convolutions and pooling?
- (done): Coding convolutions and pooling layers
- (done): Implementing convolutional layers
- (done): Learn more about convolutions
- (done): Implementing pooling layers
- (done): Getting hands-on, your first ConvNet
- (done): Improving the Fashion classifier with convolutions
- (done): Try it for yourself
- (done): Walking through convolutions
- (done): Experiment with filters and pools
2020-08-26
- (done): Week 3 Quiz
- (done): Exercise 3 (Improve MNIST with convolutions)
- (done): Week 3 Resources
- (done): Exercise 3 - Improve MNIST with convolutions
- (done): Week 4: A conversation with Andrew Ng
- (done): Explore an impactful, real-world solution
- (done): Understanding ImageGenerator
- (done): Designing the neural network
- (done): Defining a ConvNet to use complex images
- (done): Train the ConvNet with ImageGenerator
- (done): Training the ConvNet with fit_generator
- (done): Exploring the solution
- (done): Walking through developing a ConvNet
- (done): Training the neural network
- (done): Walking through training the ConvNet with fit_generator
- (done): Experiment with the horse or human classifier
- (done): Adding automatic validation to test accuracy
- (done): Get hands-on and use validation
- (done): Exploring the impact of compressing images
- (done): Get Hands-on with compacted images
- (done): Week 4 Quiz
- (done): Exercise 4 (Handling complex images)
- (done): Programming Assignment: Exercise 4 (Handling complex images)
- (done): Week 4 Resources
- (done): Exercise 4 - Handling complex images
- (done): Wrap up
https://www.coursera.org/learn/convolutional-neural-networks-tensorflow
2020-08-27
- (done): Week 1 Introduction, A conversation with Andrew Ng
- (done): Before you Begin: TensorFlow 2.0 and this Course
- (done): A conversation with Andrew Ng
- (done): The cats vs dogs dataset
- (done): Training with the cats vs. dogs dataset
- (done): Looking at the notebook
- (done): Working through the notebook
- (done): What you'll see next
- (done): Fixing through cropping
- (done): Visualizing the effect of the convolutions
- (done): Looking at accuracy and loss
- (done): What have we seen so far?
- (done): Week 1 Quiz
- (done): Week 1 Wrap up
- (done): Exercise 1 - Cats vs. Dogs
2020-08-28
- (done): Week 2, A conversation with Andrew Ng
- (done): Image Augmentation (my fav subject till the moment)
- (done): Introducing augmentation
- (done): Start Coding...
- (done): Looking at the notebook
- (done): The impact of augmentation on Cats vs. Dogs
- (done): Adding augmentation to cats vs. dogs
- (done): Try it for yourself!
- (done): Exploring augmentation with horses vs. humans
- (done): What have we seen so far?
- (done): Week 2 Quiz
- (done): Week 2 Wrap up
- (done): Exercise 2 - Cats vs. Dogs using augmentation
- (done): Programming Assignment: Exercise 2 - Cats vs. Dogs using augmentation
2020-08-29
- (done): Week 3, A conversation with Andrew Ng
- (done): Understanding transfer learning: the concepts
- (done): Start coding!
- (done): Coding transfer learning from the inception mode
- (done): Adding your DNN
- (done): Coding your own model with transferred features
- (done): Using dropouts!
- (done): Exploring dropouts
- (done): Applying Transfer Learning to Cats v Dogs
- (done): Exploring Transfer Learning with Inception
- (done): What have we seen so far?
- (done): Week 3 Quiz
- (done): Week 3 Wrap up
2020-08-30
- (done) Exercise 3 - Horses vs. humans using Transfer Learning
- (done) Programming Assignment: Exercise 3 - Horses vs. humans using Transfer Learning
- (done): Week 4, A conversation with Andrew Ng
- (done): Moving from binary to multi-class classification
- (done): Introducing the Rock-Paper-Scissors dataset
- (done): Explore multi-class with Rock Paper Scissors dataset
- (done): Train a classifier with Rock Paper Scissors
- (done): Try testing the classifier
- (done): Test the Rock Paper Scissors classifier
- (done): What have we seen so far?
- (done): Week 4 Quiz
- (done): Exercise 4 - Multi-class classifier
- (done): Programming Assignment: Exercise 4 - Multi-class classifier
- (done): Exercise 4 - Multi-class classifier
- (done): Wrap up
https://www.coursera.org/learn/natural-language-processing-tensorflow
2020-08-31
- (done) Week 1, Introduction, A conversation with Andrew Ng
- (done) Introduction
- (done) Word based encodings
- (done) Using APIs
- (done) Check out the code!
- (done) Notebook for lesson 1
- (done) Text to sequence
- (done) Looking more at the Tokenizer
- (done) Padding
- (done) Notebook for lesson 2
- (done) Sarcasm, really?
- (done) Working with the Tokenizer
- (done) News headlines dataset for sarcasm detection
- (done) Check out the code!
- (done) Notebook for lesson 3
- (done) Week 1 Quiz
- (done) Week 1 Wrap up
- (done) Exercise 1- Explore the BBC news archive
- (done) Exercise 2 Answer- BBC news archive
2020-09-01
- (done) Week 2, A conversation with Andrew Ng
- (done) Introduction
- (done) The IMBD dataset
- (done) IMDB reviews dataset
- (done) Looking into the details
- (done) How can we use vectors?
- (done) More into the details
- (done) Check out the code!
- (done) Notebook for lesson 1
- (done) Remember the sarcasm dataset?
- (done) Building a classifier for the sarcasm dataset
- (done) Let’s talk about the loss function
- (done) Pre-tokenized datasets
- (done) Diving into the code (part 1)
- (done) Diving into the code (part 2)
- (done) Check out the code!
- (done) Notebook for lesson 3
- (done) Week 2 Quiz
- (done) Week 2 Wrap up
- (done) Exercise 2- BBC news archive
- (done) Exercise 2 Answer- BBC news archive
2020-09-02
- (done) Week 03, A conversation with Andrew Ng
- (done) Introduction
- (done) LSTMs
- (done) Implementing LSTMs in code
- (done) Check out the code!
- (done) Accuracy and loss
- (done) A word from Laurence
- (done) Looking into the code
- (done) Using a convolutional network
- (done) Check out the code!
- (done) Going back to the IMDB dataset
- (done) Check out the code!
- (done) Tips from Laurence
- (done) Exploring different sequence models
- (done) Week 3 Quiz
- (done) Week 3 Wrap up
- (done) Exercise 3- Exploring overfitting in NLP
2020-09-05
- (done) Week 04, A conversation with Andrew Ng
- (done) Introduction
- (done) Looking into the code
- (done) Training the data
- (done) More on training the data
- (done) Check out the code!
- (done) Notebook for lesson 1
- (done) Finding what the next word should be
- (done) Example
- (done) Predicting a word
- (done) Poetry!
- (done) Looking into the code
- (done) Laurence the poet!
- (done) Check out the code!
- (done) Your next task
- (done) Link to generating text using a character-based RNN
- (done) Week 4 Quiz
https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction
2020-09-20
- (done) Week 01, A conversation with Andrew Ng
- (done) Time series examples
- (done) Machine learning applied to time series
- (done) Common patterns in time series
- (done) Introduction to time series
- (done) Introduction to time series notebook
- (done) Train, validation and test sets
- (done) Metrics for evaluating performance
- (done) Moving average and differencing
- (done) Trailing versus centered windows
- (done) Forecasting
- (done) Forecasting notebook
- (done) Coursera Honor Code
- (done) Week 1 Wrap up
- (done) Exercise 1 - Create and predict synthetic data
2020-09-22
- (done) Week 02: A conversation with Andrew Ng
- (done) Preparing features and labels
- (done) Preparing features and labels notebook
- (done) Feeding windowed dataset into neural network
- (done) Single layer neural network
- (done) Machine learning on time windows
- (done) Prediction
- (done) More on single layer neural network
- (done) Single layer neural network notebook
- (done) Deep neural network training, tuning and prediction
- (done) Deep neural network
- (done) Deep neural network notebook
- (done) Coursera Honor Code
- (done) Week 2 Wrap up
- (done) Exercise 2 - Predict with a DNN
- (done) Exercise 2 Answer- Predict with a DNN
2020-09-27
- (done) Week 3 - A conversation with Andrew Ng
- (done) Conceptual overview
- (done) Shape of the inputs to the RNN
- (done) Outputting a sequence
- (done) Lambda layers
- (done) Adjusting the learning rate dynamically
- (done) RNN
- (done) RNN notebook
- (done) LSTM
- (done) Link to the LSTM lesson
- (done) Coding LSTMs
- (done) More on LSTM
- (done) LSTM notebook
- (done) Week 3 Quiz
- (done) Week 3 Wrap up
- (done) Exercise 3 - Mean Absolute Error
- (done) Exercise 3 Answer - Mean Absolute Error
2020-09-28
- (done) Week 4 - A conversation with Andrew Ng
- (done) Convolutions
- (done) Convolutional neural networks course
- (done) Bi-directional LSTMs
- (done) More on batch sizing
- (done) LSTM
- (done) LSTM notebook
- (done) Real data - sunspots
- (done) Train and tune the model
- (done) Prediction
- (done) Sunspots
- (done) Sunspots notebook
- (done) Combining our tools for analysis
- (done) Week 4 Quiz
- (done) Exercise 4 - Sunspots
- (done) Exercise 4 Answer - Sunspots
- (done) Week 3 Wrap up
- (done) Congratulations!
- (done) Specialization wrap up - A conversation with Andrew Ng
Silviu Daniel Eftimie - has successfully completed the online, non-credit Professional Certificate DeepLearning.AI TensorFlow Developer