Deep Learning with Python, Third Edition
$79.35
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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
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
From the Publisher
“A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work.”
Santiago Valdarrama, Founder of ml.school
“The most up-to-date and complete guide to deep learning you’ll find today!”
Aran Komatsuzaki, EleutherAI
“Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing.”
Salvatore Sanfilippo, Creator of Redis
why this book?
Deep Learning with Python, Third Edition, teaches deep learning from first principles, with hands-on, code-first examples in Python using Keras 3, plus coverage of TensorFlow, PyTorch, and JAX.
The third edition includes topics such as generative AI, transformers, diffusion models, large language models, image and text generation, and modern best practices.
The book is designed for readers with intermediate Python skills (no prior ML or deep learning experience required), offering clear explanations, intuitive visuals, and enough depth to help both beginners and experienced practitioners level up.
about Manning
Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like “In Action” and “In a Month of Lunches”, reflect a commitment to making complex topics accessible.
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Customer Reviews
4.6 out of 5 stars 373
4.2 out of 5 stars 30
4.8 out of 5 stars 6
4.6 out of 5 stars 24
4.7 out of 5 stars 4
4.4 out of 5 stars 12
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Level of proficiency
Intermediate Intermediate Intermediate Intermediate Intermediate Advanced
About the reader
Readers need intermediate Python skills and some knowledge of machine learning. For intermediate Python programmers. For intermediate Python programmers. For data scientists and ML engineers. For data scientists and data analysts. For data scientists and machine learning engineers.
Special features
Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant.
Pages
368 344 688 456 232 520
Publisher : Manning
Publication date : November 18, 2025
Edition : 3rd
Language : English
Print length : 648 pages
ISBN-10 : 1633436586
ISBN-13 : 978-1633436589
Item Weight : 1.58 pounds
Dimensions : 7.38 x 1.6 x 9.25 inches
Best Sellers Rank: #85,064 in Books (See Top 100 in Books) #15 in Computer Programming Languages #22 in Computer Neural Networks #26 in Python Programming
Customer Reviews: 5.0 5.0 out of 5 stars (10) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });
griller –
The ONE. This is the long lasting MASTERPIECE BY MR GOOGLE KERAS
This is the one book you need or must have for ai foundations.
JohnnyLanghorne –
This book is a must-have practical DL book that covers all cutting-edge techniques, including GPT.
In my opinion, this book is exceptionally captivating and well-structured.To show my appreciation to the authors, I bought 2 copies.It effectively elucidates the fundamental concepts of machine learning and deep learning within the initial chapters. From Chapter 12 onwards, the content primarily comprises new material from the latest edition. Notably, Chapter 12 delves into object detection, which has significant implications for self-driving cars. Chapters 15 – 17 explore the transformer and large language models (LLMs). Each chapter provides practical examples accompanied by Python code, making it an enjoyable experience to execute the code directly from the book.Overall, the book’s content is remarkably current, encompassing advancements in technology up until the mid-to-late 2020s. Also the author shared his best practices in machine learning throughout the whole book, which is very helpful to learners, especially to those who are just entering this new area.Having acquired over a dozen books on machine learning and deep learning, I confidently rank this book as the most comprehensive and up-to-date resource currently available. Unlike many other books, this edition boasts a substantial amount of updates and new content.Upon purchasing the paperback version, readers can also access an ebook and e-pub version.Finally, the printing quality of the book is exceptional, making it enjoyable to read both the electronic and physical versions.I highly recommend this book to anyone interested in learning about machine learning and deep learning.
Meghal Gandhi –
A practical deep learning guide!
As someone is working in machine learning, I found this new edition of Deep Learning with Python very helpful. It includes coding examples in TensorFlow, JAX, and PyTorch. Since PyTorch has become the most popular choice recently due to its pythonic style, I liked how the authors demonstrate this through practical examples and show why many researchers prefer it. It covers almost all major deep learning topics in one place like text classification, diffusion models, time series, language models, and the latest generative AI methods. The explanations are easy to follow, and the examples are practical.
PeterPan –
Great introductory book for Deep learning and various DL libraries
Deep Learning with Python is a clear, engaging, and hands‑on introduction to various deep learning approaches. With practical code examples in Keras, TensorFlow, JAX, and PyTorch, it’s ideal for beginners and also offers insights for experienced ML engineers.Chollet’s easy explanations and real‑world examples make complex deep learning concepts easy to understand, follow, and apply.
Sri S. –
A classic for Deep Learning
This a must read for those who want to understand the depths of deep-learning while experimenting with example code (mostly in Keras). This book guides a beginner with fundamental concepts and continues to advance gradually to text processing and image processing techniques leading up to transformers (foundation of most modern day LLMs). Great book for beginners and intermediate ML engineers written by none other than Francois Chollet !
Abhi –
Worth it
I liked this book more than I expected. The writing is clear, and the examples are straightforward to follow. I think it works well for beginners, but even with some experience in ML I still picked up useful insights.
brandon friar –
great read
This book is a great read. It presents a wealth of real-world information in a clear and engaging way, making the concepts easy to understand and apply.
Kishore –
A fantastic introduction to Deep Learning
This is a fantastic book. It covers from the basics to the cutting edge of Deep Learning. I particularly enjoyed the chapter on “Future of AI”. If you enjoy learning by doing, the book has code to follow along for every chapter.Fun fact: Francois Chollet (the author) is also the author of the popular Keras library, which was the first Deep Learning library I learned.