Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
KataGo in Practice: Architecture, Neural Networks, and Production-Grade Go AI

KataGo in Practice: Architecture, Neural Networks, and Production-Grade Go AI

Chekkal, Samir

Paperback

Series: In Practice

General Computers

ISBN13: 9798258998149
Publisher: Independently Published
Published: Apr 26 2026
Pages: 152
Weight: 0.47
Height: 0.33 Width: 6.00 Depth: 9.00
Language: English
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

Also from

Chekkal, Samir

View All

Also in

General Computers

View All