개요
비전통적이고 직관 우선의 교과서로, 기초부터 고급 수학, 컴퓨팅, 인공지능을 다룹니다. 깊이 있는 이해를 추구하는 실무자를 대상으로 하며, 벡터, 행렬, 미적분학, 머신러닝, 자연어 처리, 컴퓨터 비전 등의 장을 포함합니다.
README 미리보기
# Maths, CS & AI Compendium\n\n\n\n**Read online**: [henryndubuaku.github.io/maths-cs-ai-compendium](https://henryndubuaku.github.io/maths-cs-ai-compendium/)\n\n## Overview\nMost textbooks bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI. This is an open, unconventional textbook covering maths, computing, and artificial intelligence from the ground up. Written for curious practitioners looking to deeply understand the stuff, not just survive an exam/interview. \n\n## Background\nOver the past years working in AI/ML, I filled notebooks with intuition first, real-world context, no hand-waving explanations of maths, computing and AI concepts. In 2025, a few friends used these notes to prep for interviews at DeepMind, OpenAI, Nvidia etc. They all got in and currently perform well in their roles. Meanwhile I got in Y Combinator last year. So I'm sharing to everyone.\n\n## MCP Server\nThis repo includes an MCP server that lets any AI assistant (Claude Code, Cursor, VS Code, etc.) use the compendium as a knowledge base. It requires a local clone of the repo. Comes with tools for educational purposes and example implementations.\n\n## Outline \n\n| # | Chapter | Summary | Status |\n|---|---------|---------|--------|\n| 01 | [Vectors](chapter%2001%3A%20vectors/01.%20vector%20spaces.md) | Spaces, magnitude, direction, norms, metrics, dot/cross/outer products, basis, duality | Available |\n| 02 | [Matrices](chapter%2002%3A%20matrices/01.%20matrix%20properties.md) | Properties, special types, operations, linear transformations, decompositions (LU, QR, SVD) | Available |\n| 03 | [Calculus](chapter%2003%3A%20calculus/01.%20differential%20calculus.md) | Derivatives, integrals, multivariate calculus, Taylor approximation, optimisation and gradient descent | Available |\n| 04 | [Statistics](chapter%2004%3A%20statistics/01.%20fundamentals.md) | Descriptive measures, sampling, ce