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Go AI Guide for Engineers

Welcome to the Go AI technical documentation section! This area provides comprehensive technical resources and guides for engineers and developers who want to deeply understand, deploy, or develop Go AI.

Section Contents​

This section covers the following topics:

Background Knowledge​

  • AlphaGo Paper Analysis: In-depth analysis of DeepMind's breakthrough research, including Policy Network, Value Network, and MCTS integration
  • KataGo Paper Analysis: Understanding the innovative design of the current most advanced open-source Go AI
  • Other Go AI Introduction: Comprehensive comparison of commercial and open-source Go AIs

KataGo in Practice​

  • Installation and Setup: Building KataGo environment from scratch on various platforms
  • Common Commands: Practical guide to GTP protocol and Analysis Engine
  • Source Code Architecture: Deep exploration of KataGo's code structure and implementation details

Who Should Read This​

This section is suitable for the following readers:

Reader TypeRecommended Content
Software EngineersWant to integrate Go AI into projects → Start with "Installation and Setup"
Machine Learning EngineersWant to understand Go AI algorithms → Start with "AlphaGo Paper Analysis"
ResearchersWant to conduct Go AI research → Read all background knowledge then dive into source code architecture
Go App DevelopersWant to develop Go-related applications → Focus on "Common Commands" and "Analysis Engine"
System AdministratorsNeed to deploy Go AI services → Focus on "Installation and Setup" chapter

Suggested Learning Paths​

Based on your goals, we suggest the following learning paths:

Path A: Quick Start (1-2 days)​

For developers who want to quickly deploy KataGo:

  1. KataGo Installation and Setup - Set up the environment
  2. KataGo Common Commands - Learn basic operations

Path B: Deep Understanding (1-2 weeks)​

For engineers who want complete understanding of Go AI technology:

  1. AlphaGo Paper Analysis - Understand basic architecture
  2. KataGo Paper Analysis - Learn latest improvements
  3. Other Go AI Introduction - Know the industry ecosystem
  4. KataGo Installation and Setup - Hands-on practice
  5. KataGo Common Commands - Deep dive into features

Path C: Development Contribution (1+ month)​

For those who want to contribute to KataGo open source or develop their own Go AI:

  1. Complete all content in Path B
  2. KataGo Source Code Architecture - Dive into the code
  3. Read KataGo GitHub Issues and Pull Requests
  4. Try modifications and experiments

Prerequisites​

To smoothly read this section, we recommend having the following background:

  • Programming: Familiar with at least one programming language (Python, C++ preferred)
  • Machine Learning Basics: Understanding of neural networks, backpropagation, and other basic concepts
  • Go Rules: Know basic Go rules and terminology
  • Command Line: Familiar with basic terminal operations

You can still read without these prerequisites, but may need to look up additional resources.

Start Exploring​

Ready? Start your Go AI technical journey with Background Knowledge!

If you already have machine learning background and want to get hands-on quickly, go directly to KataGo Practical Getting Started Guide.