Deep Learning with Python, Third Edition

Francois Chollet, Matthew Watson

• Publisher: Manning
• Year: 2025
• eBook ISBN:
  9781638357773
• Subtopic: Neural Networks

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!

Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.

In Deep Learning with Python, Third Edition you’ll discover:

• Deep learning from first principles
• The latest features of Keras 3
• A primer on JAX, PyTorch, and TensorFlow
• Image classification and image segmentation
• Time series forecasting
• Large Language models
• Text classification and machine translation
• Text and image generation—build your own GPT and diffusion models!
• Scaling and tuning models

With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images.

About the technology

In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.

About the book

Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer.

What's inside

• Hands-on, code-first learning
• Comprehensive, from basics to generative AI
• Intuitive and easy math explanations
• Examples in Keras, PyTorch, JAX, and TensorFlow

About the reader

For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.

About the author

François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras.

Table of Contents

1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to TensorFlow, PyTorch, JAX, and Keras
4 Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 A deep dive on Keras
8 Image classification
9 ConvNet architecture patterns
10 Interpreting what ConvNets learn
11 Image segmentation
12 Object detection
13 Timeseries forecasting
14 Text classification
15 Language models and the Transformer
16 Text generation
17 Image generation
18 Best practices for the real world
19 The future of AI
20 Conclusions

Table of contents

• Praise for the Second Editio
• Deep Learning with Python, Third Edition
• copyright
• contents
• preface
• acknowledgments
• about this book
• about the authors
• about the cover illustration
• 1 What is deep learning?
• 2 The mathematical building blocks of neural networks
• 3 Introduction to TensorFlow, PyTorch, JAX, and Keras
• 4 Classification and regression
• 5 Fundamentals of machine learning
• 6 The universal workflow of machine learning
• 7 A deep dive on Keras
• 8 Image classification
• 9 ConvNet architecture patterns
• 10 Interpreting what ConvNets learn
• 11 Image segmentation
• 12 Object detection
• 13 Timeseries forecasting
• 14 Text classification
• 15 Language models and the Transformer
• 16 Text generation
• 17 Image generation
• 18 Best practices for the real world
• 19 The future of AI
• 20 Conclusions