Isbn: 9798258998149 - katago in practice: architecture, neural networks, and production-grade go ai (5 risultati)

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  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798258998149

    Serie: Libro 18 di 76 - In Practice

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    EUR 22,81

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    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798258998149

    Serie: Libro 18 di 76 - In Practice

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    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    EUR 21,51

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    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798258998149

    Serie: Libro 18 di 76 - In Practice

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    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

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    Paperback. Condizione: new. Paperback. KataGo is the strongest open-source Go engine ever built -- and the most instructive case study in modern AI engineering. This book takes you inside every layer of the system, from raw board positions to production-grade inference, giving you the deep technical understanding that no tutorial or README can provide. Whether you are a machine learning engineer curious about how AlphaZero-style self-play actually works at scale, a Go enthusiast who wants to understand what your analysis engine is really doing, or a software architect studying how research prototypes become production systems, this book was written for you. What you will learn: How KataGo's neural network architecture evolved from simple residual towers to the global-pooling and nested-bottleneck designs that outperform DeepMind's original AlphaGo ZeroThe complete Monte Carlo Tree Search (MCTS) pipeline -- from UCB selection and virtual losses to the PUCT formula and how KataGo's search differs from vanilla AlphaZeroKataGo's self-play training loop: game generation, position sampling, data augmentation, and the curriculum strategies that let it reach superhuman strength on consumer hardwareOwnership, territory, and score estimation heads -- the auxiliary predictions that make KataGo uniquely useful for analysis and teachingThe GTP protocol, KataGo's analysis engine, and how to integrate the engine into your own applications via JSON queriesProduction deployment patterns: TensorRT and OpenCL backends, batched inference, multi-GPU scaling, and performance tuning for real-world workloadsHow to extend and modify KataGo -- custom training runs, network surgery, rule variants, and contributing to the open-source project21 original diagrams map neural network data flows, search trees, training pipelines, and deployment architectures so you can see the system, not just read about it. Every chapter connects theory to code. You will not just learn what KataGo does -- you will understand why each design decision was made, what alternatives were considered, and how the pieces fit together into one of the most impressive AI engineering achievements in the open-source world. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Lingua: Inglese

    Editore: Independently published, 2026

    9798258998149

    Serie: Libro 18 di 76 - In Practice

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    • Print on Demand

    Da: California Books, Miami, FL, U.S.A.California Books

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    EUR 22,32

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    Condizione: New. Print on Demand.

  • Lingua: Inglese

    Editore: Independently Published, 2026

    9798258998149

    Serie: Libro 18 di 76 - In Practice

    • Brossura
    • Print on Demand

    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

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    EUR 25,24

    EUR 43,20 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 1 disponibili

    Paperback. Condizione: new. Paperback. KataGo is the strongest open-source Go engine ever built -- and the most instructive case study in modern AI engineering. This book takes you inside every layer of the system, from raw board positions to production-grade inference, giving you the deep technical understanding that no tutorial or README can provide. Whether you are a machine learning engineer curious about how AlphaZero-style self-play actually works at scale, a Go enthusiast who wants to understand what your analysis engine is really doing, or a software architect studying how research prototypes become production systems, this book was written for you. What you will learn: How KataGo's neural network architecture evolved from simple residual towers to the global-pooling and nested-bottleneck designs that outperform DeepMind's original AlphaGo ZeroThe complete Monte Carlo Tree Search (MCTS) pipeline -- from UCB selection and virtual losses to the PUCT formula and how KataGo's search differs from vanilla AlphaZeroKataGo's self-play training loop: game generation, position sampling, data augmentation, and the curriculum strategies that let it reach superhuman strength on consumer hardwareOwnership, territory, and score estimation heads -- the auxiliary predictions that make KataGo uniquely useful for analysis and teachingThe GTP protocol, KataGo's analysis engine, and how to integrate the engine into your own applications via JSON queriesProduction deployment patterns: TensorRT and OpenCL backends, batched inference, multi-GPU scaling, and performance tuning for real-world workloadsHow to extend and modify KataGo -- custom training runs, network surgery, rule variants, and contributing to the open-source project21 original diagrams map neural network data flows, search trees, training pipelines, and deployment architectures so you can see the system, not just read about it. Every chapter connects theory to code. You will not just learn what KataGo does -- you will understand why each design decision was made, what alternatives were considered, and how the pieces fit together into one of the most impressive AI engineering achievements in the open-source world. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.