{ "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, "cover": "https://listme.name/assets/covers/deep-learning-book.webp", "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": "hands-on-machine-learning", "name": "Hands-On Machine Learning", "kind": "book", "website": "https://openlibrary.org/works/OL20709638W", "page": "https://listme.name/books/hands-on-machine-learning", "tagline": "Practical machine learning with scikit-learn, Keras and TensorFlow", "summary": "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a book by Aurélien Géron, published by O'Reilly Media. Its subtitle is Concepts, Tools, and Techniques to Build Intelligent Systems. The 2022 O'Reilly editions in the Open Library record run to about 850 pages.", "developer": "O'Reilly Media", "author": "Aurélien Géron", "formats": [ "print" ], "pricing": "paid", "price_note": "Print editions from O'Reilly; price varies by seller", "free_to_use": false, "open_source": false, "strengths": [ "Teaches methods through code", "Covers scikit-learn, Keras and TensorFlow", "Recent 2022 edition" ], "limits": [ "Less mathematical depth than the theory texts", "No free full text online" ], "checked": "2026-10-10", "connected_to_publisher": false, "cover": "https://listme.name/assets/covers/hands-on-machine-learning.webp", "overview": "The book teaches machine learning in Python with three libraries. Scikit-learn covers classical models, and Keras and TensorFlow cover neural networks, so readers see both kinds of method in code. The subtitle frames it as concepts, tools and techniques for building intelligent systems, and the hands-on method is central to how it teaches.\n\nO'Reilly publishes it in print, and the 2022 editions in the Open Library record run to about 850 pages. It is not free to read online. The book gives less mathematical depth than the theory texts in this list, but it is more practical than most of them, which makes it a useful companion to Deep Learning and Probabilistic Machine Learning." } ], "differences": [ "Deep Learning has a free plan or free version; Hands-On Machine Learning does not.", "Deep Learning: free. Hands-On Machine Learning: 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": 13, "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": "hands-on-machine-learning", "rank": 6, "score": 87, "verdict": "Practical code for classical models and neural networks, built on scikit-learn, Keras and TensorFlow. It is paid with no free text, and it goes less deep into theory than the books above it.", "best_for": "Building models with scikit-learn and TensorFlow", "spec": { "level": "Intermediate", "free_online": false, "latest_edition": "2022, O'Reilly", "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-hands-on-machine-learning", "json": "https://listme.name/api/v1/compare/deep-learning-book-vs-hands-on-machine-learning.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" }