A comprehensive AI Knowledge Base covering machine learning, NLP, deep learning, and mathematical foundations. Systematizing fragmented frontier learning into an interconnected graph.
AI Knowledge Graph is a systematic, full-stack AI knowledge base designed to bridge foundational theories with frontier applications. It extensively covers core domains including machine learning, deep learning, reinforcement learning, as well as their underlying mathematical foundations (e.g., linear algebra, probability, and numerical optimization).
When diving deep into Artificial Intelligence, learners consistently encounter two major bottlenecks: the highly fragmented nature of algorithms and the incredibly heavy mathematical background required. These knowledge gaps act as significant barriers to mastering frontier technologies and progressing in AI research.
To counter this, we established this structured knowledge map. It aims to help researchers and developers systematically organize, connect, and visualize foundational to advanced concepts in AI research. Whether your goal involves Natural Language Processing (NLP), Computer Vision (CV), Computer Graphics (CG), or downstream deep learning applications, this wiki serves to integrate essential pieces of knowledge comprehensively.
This is a continuously evolving open-source knowledge ecosystem. It may not be perfect right now, but we sincerely welcome researchers and enthusiasts to contribute by submitting Pull Requests (PR). Let's learn and grow together!
Explore the knowledge base through several structured paths:
We hope this platform becomes a lifelong companion for AI learners—turning every fragmented note into an interconnected node, thereby building your very own macro universe of AI understanding.