Overview

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 Zero The 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 AlphaZero KataGo's self-play training loop: game generation, position sampling, data augmentation, and the curriculum strategies that let it reach superhuman strength on consumer hardware Ownership, territory, and score estimation heads -- the auxiliary predictions that make KataGo uniquely useful for analysis and teaching The GTP protocol, KataGo's analysis engine, and how to integrate the engine into your own applications via JSON queries Production deployment patterns: TensorRT and OpenCL backends, batched inference, multi-GPU scaling, and performance tuning for real-world workloads How to extend and modify KataGo -- custom training runs, network surgery, rule variants, and contributing to the open-source project 21 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.

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