fchollet--deep-learning-with-python-notebooks/second_edition/README.md

Second edition notebooks

These are the notebooks for the second edition of the book, originally published in 2021. These notebooks use tf.keras with TensorFlow 2.16.

Table of contents

• Chapter 2: The mathematical building blocks of neural networks
• Chapter 3: Introduction to Keras and TensorFlow
• Chapter 4: Getting started with neural networks: classification and regression
• Chapter 5: Fundamentals of machine learning
• Chapter 7: Working with Keras: a deep dive
• Chapter 8: Introduction to deep learning for computer vision
• Chapter 9: Advanced deep learning for computer vision
  • Part 1: Image segmentation
  • Part 2: Modern convnet architecture patterns
  • Part 3: Interpreting what convnets learn
• Chapter 10: Deep learning for timeseries
• Chapter 11: Deep learning for text
  • Part 1: Introduction
  • Part 2: Sequence models
  • Part 3: Transformer
  • Part 4: Sequence-to-sequence learning
• Chapter 12: Generative deep learning
  • Part 1: Text generation
  • Part 2: Deep Dream
  • Part 3: Neural style transfer
  • Part 4: Variational autoencoders
  • Part 5: Generative adversarial networks
• Chapter 13: Best practices for the real world
• Chapter 14: Conclusions