Deep Learning with Python versus Generative Deep Learning.
Both show up on every "best" list. They're not competitors. They're a sequence. Here's which one to read first, and when.
Reviewed by Ashish Sheth · Updated May 2026
Author
François Chollet, Matthew Watson
David Foster
Pages
1250
453
Published
2025
2023
Publisher
Manning Publications
Shroff Publishers / O'Reilly Media
Level
intermediate
intermediate
Amazon Rating
4.5/5 (25)
4.5/5 (205)
Goodreads Rating
4.57/5 (1,428)
4.3/5 (264)
Deep Learning with Python
Strengths
+ Written by the creator of Keras — authority is unmatched
+ 3rd edition (2025) adds JAX, PyTorch, generative AI, and Keras 3 multi-backend
+ Clear, code-driven explanations without unnecessary math
+ Develops genuine intuition, not just recipe-following
Caveats
− 1,250 pages — significant time commitment
− Keras-first framing may feel indirect if you live in pure PyTorch
− Goes broad rather than deep on the newest LLM-era techniques (pair with AI Engineering for production LLM work)
Generative Deep Learning
Strengths
+ Best single book covering the breadth of generative architectures
+ 2nd edition adds diffusion models — essential for 2026 readers
+ Code-first with Keras implementations you can run
+ Strong theoretical grounding without being math-heavy
Caveats
− Keras/TensorFlow focus when much of generative ML is now PyTorch
− Diffusion chapter is solid but the field has moved fast since 2023
− Less coverage of LLM generation than readers may expect from the title
The verdict
Deep Learning with Python is the stronger pick overall, but Generative Deep Learning may suit you better if you're a developers exploring generative models.
Deep Learning with Python
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Generative Deep Learning
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Frequently asked
Which is better, Deep Learning with Python or Generative Deep Learning?
Deep Learning with Python is the stronger pick overall, but Generative Deep Learning may suit you better if you're a developers exploring generative models.
How is the 3rd edition different from the 2nd?
The 3rd edition (October 2025) adds Keras 3 multi-backend support, PyTorch and JAX primers, and full coverage of modern generative AI. It's also significantly longer (1,250 vs 504 pages). If you read the 2nd edition recently, the new content is the main reason to upgrade.
Does this book cover Stable Diffusion and modern image generation?
The 2nd edition (2023) added a diffusion-models chapter that covers the architecture behind Stable Diffusion. Specific tools and APIs have evolved since, but the architecture explanations hold up.