{
    "products": [
        {
            "slug": "bandit-algorithms",
            "name": "Bandit Algorithms",
            "kind": "book",
            "website": "https://tor-lattimore.com/",
            "page": "https://listme.name/books/bandit-algorithms",
            "tagline": "Free PDF of a theory text on bandit problems and exploration",
            "summary": "Bandit Algorithms is a book by Tor Lattimore and Csaba Szepesvári, published by Cambridge University Press in 2020. It covers the theory of bandit problems, in which an agent chooses actions and learns from rewards. The authors' site links a free PDF.",
            "developer": "Cambridge University Press",
            "author": "Tor Lattimore and Csaba Szepesvári",
            "formats": [
                "pdf",
                "print"
            ],
            "pricing": "free",
            "price_note": "Free PDF from the authors; printed copies sold separately",
            "free_to_use": true,
            "open_source": false,
            "strengths": [
                "Free PDF from the authors",
                "Rigorous treatment of bandit theory",
                "Published by Cambridge University Press"
            ],
            "limits": [
                "Theory-first, with little practical code",
                "Narrower than a general reinforcement learning book"
            ],
            "checked": "2026-10-10",
            "connected_to_publisher": false,
            "cover": "https://listme.name/assets/covers/bandit-algorithms.webp",
            "overview": "The book treats bandit problems, in which an agent repeatedly chooses among actions with uncertain rewards and must balance trying new options against using what it already knows. It covers the theory behind that trade-off and the algorithms that handle it, with analysis of how well they perform. Bandits are the simplest setting in which exploration matters, so the book is a foundation for reinforcement learning theory.\n\nThe PDF is free from the authors' site, and the printed book is published by Cambridge University Press. It is one of the free theory texts in this list, alongside Sutton and Barto and the Bertsekas book. It is a theory text rather than a practical guide, so readers who want the wider field should start with Sutton and Barto, and readers who want code should look at the deep reinforcement learning books here."
        },
        {
            "slug": "tinyml-book",
            "name": "TinyML",
            "kind": "book",
            "website": "https://tinymlbook.com/",
            "page": "https://listme.name/books/tinyml-book",
            "tagline": "Practical guide to running machine learning on microcontrollers",
            "summary": "TinyML is a practical book by Pete Warden and Daniel Situnayake on training small machine learning models and running them on microcontrollers and other resource-constrained devices. The book's official site offers a free preview PDF and sells the full book as a PDF, and a printed copy is available from Amazon.",
            "author": "Pete Warden and Daniel Situnayake",
            "formats": [
                "pdf",
                "print"
            ],
            "pricing": "paid",
            "price_note": "Free preview PDF; full PDF and print copy sold separately",
            "free_to_use": false,
            "open_source": false,
            "strengths": [
                "Practical guide to machine learning on microcontrollers",
                "Free preview PDF on the official site",
                "Covers the path from a trained model to a device"
            ],
            "limits": [
                "The free version is a preview, not the full book",
                "Little mathematical theory"
            ],
            "checked": "2026-10-10",
            "connected_to_publisher": false,
            "cover": "https://listme.name/assets/covers/tinyml-book.webp",
            "overview": "The book explains how to shrink trained models so they fit on microcontrollers, and how to deploy them to small embedded devices. The official site describes it as a guide to deep learning on resource-constrained devices, where machine learning meets embedded systems. Its material is practical and code-first, aimed at developers who build products with small hardware.\n\nThe site offers a free preview PDF and sells the full text as a PDF. The book is not a theory text, so readers who want the mathematics behind training will need a different book. Its value is in the engineering path from a trained model to a working device, which is the part most general machine learning books skip."
        }
    ],
    "differences": [
        "Bandit Algorithms has a free plan or free version; TinyML does not.",
        "Bandit Algorithms: free. TinyML: paid."
    ],
    "shared_rankings": [
        {
            "list": "reinforcement-learning-and-edge-ml-books",
            "name": "Reinforcement learning and edge ML books",
            "page": "https://listme.name/best/reinforcement-learning-and-edge-ml-books",
            "of": 10,
            "results": [
                {
                    "product": "bandit-algorithms",
                    "rank": 4,
                    "score": 88,
                    "verdict": "A free PDF from the authors and a Cambridge University Press text on bandit theory, the simplest setting where exploration matters. It is theory-first and narrower than Sutton and Barto, so it ranks below that standard text.",
                    "best_for": "Theory of exploration and bandit problems",
                    "spec": {
                        "level": "Advanced",
                        "free_online": true,
                        "latest_edition": "2020, Cambridge University Press",
                        "math_depth": "Heavy: probability and proofs"
                    }
                },
                {
                    "product": "tinyml-book",
                    "rank": 3,
                    "score": 89,
                    "verdict": "The most practical guide here for running models on microcontrollers, with a free preview. The full book is paid and has little theory, unlike the Sze book.",
                    "best_for": "Running machine learning on microcontrollers",
                    "spec": {
                        "level": "Intermediate",
                        "free_online": false,
                        "latest_edition": "Not stated on the book site",
                        "math_depth": "Light: basic programming"
                    }
                }
            ]
        }
    ],
    "ranks_higher_in_closest_shared_list": "tinyml-book",
    "page": "https://listme.name/compare/bandit-algorithms-vs-tinyml-book",
    "json": "https://listme.name/api/v1/compare/bandit-algorithms-vs-tinyml-book.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"
}