maths-cs-ai-compendium

Overview

An unconventional, intuition-first textbook that covers mathematics, computing, and artificial intelligence from basic to advanced levels. Designed for practitioners seeking deep understanding, with chapters on vectors, matrices, calculus, machine learning, NLP, computer vision, and more.

README Preview

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bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI.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