This repository hosts a collection of open-source courses and tutorials on Artificial Intelligence, Machine Learning, and Computer Vision, primarily aimed at students and practitioners seeking to learn foundational and advanced concepts. It provides comprehensive Chinese-language explanations, code implementations, and paper analyses for key AI topics, enabling self-study and project development.

How It Works

The project offers structured learning paths, including detailed video lectures and accompanying code repositories. It covers seminal courses like Stanford's CS231N (Computer Vision) and CS224W (Graph Machine Learning), alongside practical guides for AI graduation projects such as image classification, semantic segmentation, and keypoint detection. The approach emphasizes hands-on coding and in-depth understanding of algorithms and research papers.

Quick Start & Requirements

• Installation: Primarily involves cloning GitHub repositories and installing Python dependencies via pip. Specific project requirements vary, but generally include Python 3.8+ and common ML libraries like PyTorch or TensorFlow.
• Prerequisites: Access to Bilibili for video content is recommended. Some projects may require specific hardware (e.g., GPUs for training) or datasets.
• Resources: Links to Bilibili playlists and GitHub code repositories are provided for each course.

Highlighted Details

• Comprehensive Chinese-language explanations of Stanford's CS231N and CS224W courses.
• Practical, end-to-end tutorials for AI graduation projects (image classification, semantic segmentation, keypoint detection).
• In-depth analysis and code reproduction of classic AI papers.
• Introduction to explainable AI (XAI) with algorithm reviews and code examples.

Maintenance & Community

The project is maintained by TommyZihao, with content primarily delivered through Bilibili. Community interaction and support are likely channeled through Bilibili comments or associated GitHub issues.

Licensing & Compatibility

The repository itself appears to be under a permissive license, likely MIT, given its nature as educational material and code sharing. However, specific code snippets or datasets within individual project folders might have different licenses. Compatibility for commercial use should be verified on a per-project basis.

Limitations & Caveats

The primary language of instruction is Chinese, which may be a barrier for non-Chinese speakers. While code is provided, the focus is on educational content, and production-ready deployment might require additional engineering effort.