Deep Learning with Python versus Natural Language Processing with Transformers.
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
Option B
Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra, Thomas Wolf · 2022
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Author
François Chollet, Matthew Watson
Lewis Tunstall, Leandro von Werra, Thomas Wolf
Pages
1250
408
Published
2025
2022
Publisher
Manning Publications
O'Reilly Media
Level
intermediate
intermediate
Amazon Rating
4.5/5 (25)
4.6/5 (257)
Goodreads Rating
4.57/5 (1,428)
4.39/5 (212)
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)
Natural Language Processing with Transformers
Strengths
+ Written by the team that built the Transformers library — definitive authority
+ Code-first with runnable Hugging Face examples throughout
+ Covers the full lifecycle: pretrain, fine-tune, evaluate, deploy
+ Excellent for engineers who want to actually ship NLP features
Caveats
− Predates the LLM/GPT-4 era — emphasis is on smaller fine-tuned models
− Some library APIs have evolved since publication
− Less coverage of generative LLMs than the title implies for 2026 readers
The verdict
Choose based on your specific needs: Deep Learning with Python focuses on deep learning from first principles, while Natural Language Processing with Transformers emphasizes transformer architecture in depth.
Deep Learning with Python
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Natural Language Processing with Transformers
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Frequently asked
Which is better, Deep Learning with Python or Natural Language Processing with Transformers?
Choose based on your specific needs: Deep Learning with Python focuses on deep learning from first principles, while Natural Language Processing with Transformers emphasizes transformer architecture in depth.
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.
Is NLP with Transformers still relevant given how fast LLMs evolved?
The transformer architecture chapters and Hugging Face workflows are still the standard. What's outdated: emphasis on BERT-era fine-tuning over GPT-style prompting. Pair with a current LLM book.