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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
| Date | Stars |
|---|---|
| 2026-07-24 | 7126 |
| 2026-07-25 | 7136 |
| 2026-07-28 | 7169 |
| 2026-07-30 | 7169 |
| 2026-08-06 | 7169 |
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# Maths, CS & AI Compendium <img src="images/logo.png" alt="Logo" style="border-radius: 30px; width: 100%;"> **Read online**: [henryndubuaku.github.io/maths-cs-ai-compendium](https://henryndubuaku.github.io/maths-cs-ai-compendium/) <a href="https://trendshift.io/repositories/21344?utm_source=repository-badge&utm_medium=badge&utm_campaign=badge-repository-21344" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/repositories/21344" alt="HenryNdubuaku%2Fmaths-cs-ai-compendium | Trendshift" width="250" height="55"/></a> ## Overview Most 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bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI.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. ## Background Over 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. ## MCP Server This 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. ## Outline | # | Chapter | Summary | Status | |---|---------|---------|--------| | 01 | [Vectors](chapter%2001%20-%20vectors/01.%20vector%20spaces.md) | Spaces, magnitude, direction, norms, metrics, dot/cross/outer products, basis, duality | Available | | 02 | [Matrices](chapter%2002%20-%20matrices/01.%20matrix%20properties.md) | Properties, special types, operations, linear transformations, decompositions (LU, QR, SVD) | Available | | 03 | [Calculus](chapter%2003%20-%20calculus/01.%20differential%20calculus.md) | Derivatives, integrals, multivariate calculus, Taylor approximation, optimisation and gradient descent | Available | | 04 | [Statistics](chapter%2004%20-%20statistics/01.%20fundamentals.md) | Descriptive measures, sampling, central limit theorem, hypothesis testing, confidence intervals | Available | | 05 | [Probability](chapter%2005%20-%20probability/01.%20counting.md) | Counting, conditional probability, distributions, Bayesian methods, information theory | Available | | 06 | [Machine Learning](chapter%2006%20-%20machine%20learning/01.%20classical%20machine%20learning.md) | Classical ML, gradient methods, deep learning, reinforcement learning, distributed training | Available | | 07 | [Computational Linguistics](chapter%2007%20-%20computational%20linguistics/01.%20linguistic%20foundations.md) | syntax, semantics, pragmatics, NLP, language models, RNNs, CNNs, attention, transformers, text diffusion, text OCR, MoE, SSMs, modern LLM architectures, NLP evaluation | Available | | 08 | [Computer Vision](chapter%2008%20-%20computer%20vision/01.%20image%20fundamentals.md) | image processing, object detection, segmentation, video processing, SLAM, CNNs, vision transformers, diffusion, flow matching, VR/AR | Available | | 09 | [Audio & Speech](chapter%2009%20-%20audio%20and%20speech/01.%20digital%20signal%20processing.md) | DSP, ASR, TTS, voice & acoustic activity detection, diarisation, source separation, active noise cancellation, wavenet, conformer | Available | | 10 | [Multimodal Learning](chapter%2010%20-%20multimodal%20learning/01.%20multimodal%20representations.md) | fusion strategies, contrastive learning, CLIP, VLMs, image/video tokenisation, cross-modal generation, unified architectures, world models | Available | | 11 | [Autonomous Systems](chapter%2011%20-%20autonomous%20systems/01.%20perception.
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Henry Ndubuaku · Cactus Compute · United Kingdom
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Hemanth HM · @paypal · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:90a7eb59ac937f31, topic:deep-learning, topic:jax, readme:distributed training
matched fp:90a7eb59ac937f31, topic:computer-vision, readme:computer vision, readme:object detection
matched fp:90a7eb59ac937f31, topic:nlp
matched fp:90a7eb59ac937f31, topic:reinforcement-learning, readme:reinforcement learning