https://www.coursera.org/specializations/tensorflow-in-practice?#courses
1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning:
- machine learning;
- neural network;
- loss function;
- optimizers;
- training;
- predict;
- convergence;
- epochs;
- neurons;
- convolutional neural network;
- filter;
- pooling;
- confinet (ConvNet);
- image generator (TF);
- learning rate;
- data augmentation;
- tensorFlow's Image Generation lib;
- transfer learning;
- inception network;
- multicast learning;
- image data generator;
- image augmentation;
- Inception model;
- Fine-Tuning;
- Feature Extraction;
- dropouts;
- multi-class classifiers;
- natural language processing;
- tokens (keras Tokenizer);
- hyperparameter;
- encode sentences;
- embeddings;
- word embeddings;
- TensorFlow Data Services or TFTS;
- eager_execution;
- Pre-tokenized;
- recurrent neural networks;
- embedding dimension;
- Long short-term memory (LSTM);
- Cell State;
- Gated Recurrent Units (GRU);
- sequence models
- sunspot activity
- dense model
- stateless vs stateful
- imputation (project back into the past to see how we got to where we are now or fill missing data)
- trend - an overall direction for data regardless of direction
- seasonality - a regular change in shape of the data
- autocorrelation - data that follows a predictable shape, even if the scale if diff
- noise - unpredictable changes in time series data;
- non-stationary time series - one that has a disruptive event breaking trend and seasonality
- naive forecasting
- fixed partitioning
- roll-forward partitioning
- training period / validation period / test period
- Univariate time series
- mse - square the errors and then calculate their mean.
- rmse - root means squared error
- mae - mean absolute error
- mape - mean absolute percentage error (this gives an idea of the size of the errors compared to the values)
- moving average
- differencing