Francois Chollet

François Chollet is the creator of Keras, one of the most widely used deep learning frameworks. He is currently a software engineer at Google, where he leads the Keras team. In addition, he does research on abstraction, reasoning, and how to achieve greater generality in artificial intelligence.

books by Francois Chollet

Deep Learning with R, Third Edition

  • MEAP began June 2025
  • Last updated June 2025
  • This book is in development
  • ISBN 9781633435186
  • 625 pages (estimated)
  • printed in black & white

Deep Learning with R, Third Edition introduces R programmers to the latest advances in deep learning. In it, you’ll explore how to use Keras 3 and R to build and train deep learning models, all without advanced math or low-level programming. You’ll get started on core DL tasks like computer vision and natural language processing, and you’ll take your first steps into the world of transformers, LLMs, and the foundations of modern AI.

You’ll learn to fine-tune and evaluate your models for peak performance, and dive into advanced methods like transfer learning and model interpretability. This expanded third edition brings cutting-edge coverage of transformers, building your own GPT-style language model, and creating images with diffusion models—all in R.

Deep Learning with Python, Third Edition

  • MEAP began September 2024
  • Last updated May 2025
  • Publication in September 2025 (estimated)
  • ISBN 9781633436589
  • 600 pages (estimated)
  • printed in black & white

Deep Learning with Python, Third Edition introduces deep learning from scratch. Each chapter introduces practical code examples that build up your understanding of deep learning layer by layer. You’ll appreciate the intuitive explanations, crisp color illustrations, and clear examples. In this expanded third edition you’ll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning.

Deep Learning with R, Second Edition

  • July 2022
  • ISBN 9781633439849
  • 568 pages
  • printed in black & white
  • Available translations: Russian

Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.

Deep Learning with Python, Second Edition

  • October 2021
  • ISBN 9781617296864
  • 504 pages
  • printed in color
  • Available translations: Complex Chinese, Japanese, Korean, Russian, Simplified Chinese

Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications.

Deep Learning with Python

  • November 2017
  • ISBN 9781617294433
  • 384 pages
  • printed in black & white
  • Available translations: Complex Chinese, Czech, French, German, Italian, Japanese, Korean, Polish, Russian, Simplified Chinese, Spanish, Turkish

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.