maths-cs-ai-compendium

概要

非伝統的で直感優先の教科書であり、基礎から高度な数学、コンピューティング、人工知能を網羅。深い理解を追求する実践者を対象とし、ベクトル、行列、微分積分、機械学習、自然言語処理、コンピュータビジョンなどの章を含む。

README プレビュー

# 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bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI.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