{ "products": [ { "slug": "deep-learning-book", "name": "Deep Learning", "kind": "book", "website": "https://www.deeplearningbook.org/", "page": "https://listme.name/books/deep-learning-book", "tagline": "Graduate-level textbook on deep learning from MIT Press, free online", "summary": "Deep Learning is a textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville, published by MIT Press in 2016. It covers applied mathematics, machine learning basics, deep feedforward, convolutional and recurrent networks, and research topics. The full online text is free, and the book can be ordered in print.", "author": "Ian Goodfellow, Yoshua Bengio and Aaron Courville", "formats": [ "web", "print" ], "pricing": "free", "price_note": "Full text free online; print copies sold separately", "free_to_use": true, "open_source": false, "strengths": [ "Full text free to read on the book's website", "Covers the mathematics used throughout deep learning", "Written by three researchers who work on deep learning" ], "limits": [ "Published in 2016, before transformers and large language models", "Little code; examples are mostly mathematical" ], "checked": "2026-10-10", "connected_to_publisher": false, "overview": "The book has three parts: applied mathematics and machine learning basics, practical deep networks, and research-oriented material. The table of contents on the site starts with linear algebra, probability and numerical computation before it reaches deep feedforward networks. Readers who work through it learn the notation and mathematical habits used in many later papers.\n\nThe online version is free, and the site includes exercises, lectures, a notation page and a citation template. The book dates from 2016, so it predates transformers and large language models. It suits readers who want a rigorous foundation rather than code-first tutorials, and it pairs well with a framework such as PyTorch or Keras for practice." }, { "slug": "deep-learning-with-python", "name": "Deep Learning with Python, Third Edition", "kind": "book", "website": "https://www.manning.com/books/deep-learning-with-python-third-edition", "page": "https://listme.name/books/deep-learning-with-python", "tagline": "Hands-on Python deep learning guide covering Keras 3, PyTorch and JAX", "summary": "Deep Learning with Python, Third Edition, is written by François Chollet and Matthew Watson. Manning published it in September 2025, with 648 pages, and it teaches deep learning and generative AI with Keras 3, PyTorch and JAX. Manning sells print, PDF and ePub editions.", "author": "François Chollet and Matthew Watson", "formats": [ "pdf", "epub", "print" ], "pricing": "paid", "price_note": "Print and eBook sold by Manning; no free full text", "free_to_use": false, "open_source": false, "strengths": [ "Hands-on Python code with Keras 3", "Covers generative AI in the third edition", "Current, published September 2025" ], "limits": [ "Paid, with no free full text", "Light on the mathematics behind the models" ], "checked": "2026-10-10", "connected_to_publisher": false, "overview": "The book moves from the core ideas of neural networks to training models in Keras, then to computer vision, text and generative models. The third edition adds generative AI and the Keras 3 API, which runs on TensorFlow, JAX and PyTorch. Chollet created Keras, and the book is organized as a working guide for Python developers.\n\nThe publisher lists the source code on GitHub and sells the book in print, as an eBook in PDF and ePub, and in an online format. It suits developers who want to build and train models in Python. It is lighter on theory than the Goodfellow text, so readers who need the mathematics should pair it with a formal text." } ], "differences": [ "Deep Learning has a free plan or free version; Deep Learning with Python, Third Edition does not.", "Deep Learning: free. Deep Learning with Python, Third Edition: paid." ], "shared_rankings": [ { "list": "ai-and-machine-learning-books", "name": "AI and machine learning books", "page": "https://listme.name/best/ai-and-machine-learning-books", "of": 10, "results": [ { "product": "deep-learning-book", "rank": 1, "score": 94.5, "verdict": "The canonical free textbook for the foundations of deep learning, with the full text online. It predates transformers, so pair it with a current practical guide for modern architectures.", "best_for": "Foundations of deep learning, free online", "spec": { "level": "Advanced", "free_online": true, "latest_edition": "2016, MIT Press", "math_depth": "Heavy: linear algebra and probability" } }, { "product": "deep-learning-with-python", "rank": 5, "score": 88.5, "verdict": "The strongest paid pick for writing deep learning code in Python, with Keras 3 and generative AI in the third edition. It has no free text and less theory than the free books above.", "best_for": "Practical deep learning code in Python", "spec": { "level": "Intermediate", "free_online": false, "latest_edition": "3rd edition, September 2025", "math_depth": "Light: basic Python and algebra" } } ] } ], "ranks_higher_in_closest_shared_list": "deep-learning-book", "page": "https://listme.name/compare/deep-learning-book-vs-deep-learning-with-python", "json": "https://listme.name/api/v1/compare/deep-learning-book-vs-deep-learning-with-python.json", "notes": [ "Scores are editorial and only comparable inside one list. Products with connected_to_publisher true receive a disclosed ranking lift: https://listme.name/methodology" ], "source": "https://listme.name", "license": "Free to read and cite with attribution to listme.name.", "docs": "https://listme.name/developers" }