maths-cs-ai-compendium

Why we included this project

People who work in AI/ML and want to go deeper on the math underneath the models will get a lot from this one. It starts with vector spaces, matrices, and decompositions, moves through calculus, statistics, and probability, then builds up to machine learning, NLP, computer vision, and multimodal topics. The author wrote it from notes that helped friends prepare for interviews at places like DeepMind and OpenAI, and the style leans on intuition and real-world context instead of dense notation, so it reads like something a working engineer wrote for other engineers. An MCP server also turns a local clone of the repo into a knowledge base that AI assistants can query, which makes it easy to keep at hand while you're coding.