AI Knowledge Base
This knowledge base aims to aggregate high-quality AI learning resources, reduce information fragmentation and duplication, and support collaborative maintenance and continuous updates.
Learning Paths
The knowledge base is organized around the following structure:
- AI Math Foundations: linear algebra, probability and statistics, calculus and optimization, information theory, numerical analysis
- Large Model Basics: deep learning, PyTorch, CUDA, Transformer, Embedding, introductory courses
- Reinforcement Learning: RL fundamentals, chain-of-thought (CoT), GRPO
- Foundation Models: datasets, training, fine-tuning, deployment, evaluation, model architectures
- Multimodal Large Models: LLaVA, QwenVL, ViT, MLLM
- Recommender Systems: learning paths, hands-on projects, paper resources
- Agents: LLM-based intelligent agents
- Generative Models: Diffusion Models
- Methodology: research guides, paper reading strategies
- Miscellaneous Tools: development tools, platform usage
贡献者
最近更新
Involution Hell© 2026 byCommunityunderCC BY-NC-SA 4.0
Learn
The Learn section of Involution Hell provides subject-organized study materials for AI and CS. AI topics include LLMs, Agents, reinforcement learning, multimodal models, and foundational mathematics; CS topics cover data structures, algorithms, systems programming, and backend engineering. Ideal for developers and job seekers looking to systematically build their AI/CS knowledge or prepare for technical interviews.
Agent
Explore LLM agents: OpenHands for code/shell/web automation, Kimi-Researcher for RL-driven research, and OpenAI Deep Research—for developers & AI learners.