Companion Jupyter notebooks for the book "Deep Learning with Python" (2025)

This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python, third edition by Francois Chollet and Matthew Watson.

In addition, you will also find the legacy notebooks for the second edition (2021) and the first edition (2017).

For readability, these notebooks only contain runnable code blocks and section titles, and omit everything else in the book: text paragraphs, figures, and pseudocode. If you want to be able to follow what's going on, I recommend reading the notebooks side by side with your copy of the book.

Table of contents

• Chapter 2: The mathematical building blocks of neural networks
• Chapter 3: Introduction to TensorFlow, PyTorch, JAX, and Keras
• Chapter 4: Classification and regression
• Chapter 5: Fundamentals of machine learning
• Chapter 7: A deep dive on Keras
• Chapter 8: Image Classification
• Chapter 9: Convnet architecture patterns
• Chapter 10: Interpreting what ConvNets learn
• Chapter 11: Image Segmentation
• Chapter 12: Object Detection
• Chapter 13: Timeseries Forecasting
• Chapter 14: Text Classification
• Chapter 15: Language Models and the Transformer
• Chapter 16: Text Generation
• Chapter 17: Image Generation
• Chapter 18: Best practices for the real world