For Deep Learners

This section is for engineers who want to dive deep into Go AI, covering technical implementation, theoretical foundations, and practical applications.

This section organizes its in-depth content around four areas: core technologies (neural network architecture, MCTS implementation, training mechanisms), performance optimization (GPU backends, model quantization and deployment, evaluation benchmarks), advanced topics (distributed training, custom rules, key papers), and open source and implementation. It suits engineers who want to study or contribute to Go AI in depth.

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Article Overview

Core Technologies

Article	Description
Neural Network Architecture	KataGo's residual network, input features, multi-head output design
MCTS Implementation Details	PUCT selection, virtual loss, batch evaluation, parallelization
KataGo Training Mechanism	Self-play, loss functions, training loop

Performance Optimization

Article	Description
GPU Backend & Optimization	CUDA, OpenCL, Metal backend comparison and tuning
Model Quantization & Deployment	FP16, INT8, TensorRT, cross-platform deployment
Evaluation & Benchmarking	Elo rating, match testing, SPRT statistical methods

Advanced Topics

Article	Description
Distributed Training Architecture	Self-play Worker, data collection, model release
Custom Rules & Variants	Chinese, Japanese, AGA rules, board size variants
Key Papers Guide	AlphaGo, AlphaZero, KataGo paper highlights

Open Source & Implementation

Article	Description
KataGo Source Code Guide	Directory structure, core modules, code style
Contributing to Open Source	Contribution methods, distributed training, community participation
Build Go AI from Scratch	Step-by-step implementation of a simplified AlphaGo Zero

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What Do You Want to Do?

Goal	Recommended Path
Understand neural network design	Neural Network Architecture → MCTS Implementation Details
Optimize execution performance	GPU Backend & Optimization → Model Quantization & Deployment
Research training methods	KataGo Training Mechanism → Distributed Training Architecture
Understand paper principles	Key Papers Guide → Neural Network Architecture
Hands-on coding	Build Go AI from Scratch → KataGo Source Code Guide
Contribute to open source	Contributing to Open Source → KataGo Source Code Guide

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Advanced Concept Index

When diving deep, you'll encounter the following advanced concepts:

F Series: Scaling (8)

ID	Go Concept	Physics/Math Correspondence
F1	Board size vs complexity	Complexity scaling
F2	Network size vs strength	Capacity scaling
F3	Training time vs returns	Diminishing returns
F4	Data volume vs generalization	Sample complexity
F5	Compute resource scaling	Scaling laws
F6	Neural scaling laws	Log-log relationship
F7	Large batch training	Critical batch size
F8	Parameter efficiency	Compression bounds

G Series: Dimensions (6)

ID	Go Concept	Physics/Math Correspondence
G1	High-dimensional representation	Vector space
G2	Curse of dimensionality	High-dimensional challenges
G3	Manifold hypothesis	Low-dimensional manifold
G4	Intermediate representation	Latent space
G5	Feature disentanglement	Independent components
G6	Semantic directions	Geometric algebra

H Series: Reinforcement Learning (9)

ID	Go Concept	Physics/Math Correspondence
H1	MDP	Markov chain
H2	Bellman equation	Dynamic programming
H3	Value iteration	Fixed-point theorem
H4	Policy gradient	Stochastic optimization
H5	Experience replay	Importance sampling
H6	Discount factor	Time preference
H7	TD learning	Incremental estimation
H8	Advantage function	Baseline variance reduction
H9	PPO clipping	Trust region

K Series: Optimization Methods (6)

ID	Go Concept	Physics/Math Correspondence
K1	SGD	Stochastic approximation
K2	Momentum	Inertia
K3	Adam	Adaptive step size
K4	Learning rate decay	Annealing
K5	Gradient clipping	Saturation limits
K6	SGD noise	Stochastic perturbation

L Series: Generalization & Stability (5)

ID	Go Concept	Physics/Math Correspondence
L1	Overfitting	Over-adaptation
L2	Regularization	Constrained optimization
L3	Dropout	Sparse activation
L4	Data augmentation	Symmetry breaking
L5	Early stopping	Optimal stopping

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Hardware Requirements

Reading & Learning

No special requirements, any computer will work.

Training Models

Scale	Recommended Hardware	Training Time
Mini (b6c96)	GTX 1060 6GB	Several hours
Small (b10c128)	RTX 3060 12GB	1-2 days
Medium (b18c384)	RTX 4090 24GB	1-2 weeks
Full (b40c256)	Multi-GPU cluster	Several weeks

Contributing to Distributed Training

• Any computer with a GPU can participate
• GTX 1060 or equivalent recommended minimum
• Stable internet connection required

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Getting Started

Recommended starting points:

• Want to understand principles? → Neural Network Architecture
• Want to code hands-on? → Build Go AI from Scratch
• Want to read papers? → Key Papers Guide