TensorFlow 2.x Deep Learning Python Notebooks

This repository contains Python Jupyter notebooks implementing the code samples found in the relevant deep learning literature. Note that the literature features far more content than you will find in these notebooks, primarily further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments. The notebooks have been adapted for TensorFlow 2.x and use Python 3.x.

Table of Contents

• Chapter 2:

  • 2.1: A first look at a neural network

• Chapter 3:

  • 3.5: Classifying movie reviews
  • 3.6: Classifying newswires
  • 3.7: Predicting house prices

• Chapter 4:

  • 4.4: Underfitting and overfitting

• Chapter 5:

  • 5.1: Introduction to convnets
  • 5.2: Using convnets with small datasets
  • 5.3: Using a pre-trained convnet
  • 5.4: Visualizing what convnets learn

• Chapter 6:

  • 6.1: One-hot encoding of words or characters
  • 6.1: Using word embeddings
  • 6.2: Understanding RNNs
  • 6.3: Advanced usage of RNNs
  • 6.4: Sequence processing with convnets

• Chapter 8:

  • 8.1: Text generation with LSTM
  • 8.2: Deep dream
  • 8.3: Neural style transfer
  • 8.4: Generating images with VAEs
  • 8.5: Introduction to GANs