Which book should I start with?

Python and Keras by Keras creator François Chollet, which builds tasks such as binary review classification, multi-class news classification and housing-price regression. The writer says even this one book alone noticeably improves your view of real work.

Do I have to read all three?

No. The writer says stacking all three in order is ideal but not necessary, and that starting from the one book matching where you are stuck builds momentum faster. He offers a task-based way to pick just one.

Can I use these books if my team uses PyTorch?

The code is Keras-first, so a PyTorch team would read the APIs with substitutions in mind. The writer notes the conceptual explanations do not depend on the framework, so it is not wasted effort.

Can I run the code in Advanced Deep Learning with Keras today?

It is a foreign-language book, so it must be read in English, and the code is from the TensorFlow 1.x era and needs work to run as-is. The writer still finds it useful for tracing architecture design intent.

Large Language ModelsQiita 機械学習3m agoOct 11, 2026, 10:00 JST

Keras author's book leads a 3-step deep learning reading path

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A writer who once froze in a review when asked why he picked a given hyperparameter laid out three books in order — Python and Keras by Keras creator François Chollet, Deep Learning by Goodfellow, Bengio and Courville, and Advanced Deep Learning with Keras by Rowel Atienza.

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FAQ

Which book should I start with?: Python and Keras by Keras creator François Chollet, which builds tasks such as binary review classification, multi-class news classification and housing-price regression. The writer says even this one book alone noticeably improves your view of real work.
Do I have to read all three?: No. The writer says stacking all three in order is ideal but not necessary, and that starting from the one book matching where you are stuck builds momentum faster. He offers a task-based way to pick just one.
Can I use these books if my team uses PyTorch?: The code is Keras-first, so a PyTorch team would read the APIs with substitutions in mind. The writer notes the conceptual explanations do not depend on the framework, so it is not wasted effort.
Can I run the code in Advanced Deep Learning with Keras today?: It is a foreign-language book, so it must be read in English, and the code is from the TensorFlow 1.x era and needs work to run as-is. The writer still finds it useful for tracing architecture design intent.

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