HenryNdubuaku/maths-cs-ai-compendium
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 8 contributors on GitHub.
Peaked at #34 in the weekly ranking the week of Jul 26, its best in the past year.
Use Cases & Benefits
- Provides an open, unconventional textbook covering mathematics, computing, and AI with intuitive explanations and real-world context.
- Offers deep conceptual understanding and intuition-first learning, avoiding dense notation and outdated material common in traditional textbooks.
- Use for mastering foundational math and algorithms essential for AI and machine learning research and engineering roles.
- Use for preparing effectively for technical interviews at leading AI companies with practical, intuition-driven study material.
- Use for gaining comprehensive knowledge across AI subfields like NLP, computer vision, reinforcement learning, and ML systems design.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
Watch-outs
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
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bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI.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.
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.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
947+50
Open issues
15-1
Compared with peers in Education
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 78.3K | +1.6K | 75 | 1 day ago |
| mnfst/awesome-free-llm-apis | 12.8K | +754 | 75 | — |
| luongnv89/claude-howto | 46.9K | +440 | 75 | 161 days ago |
| github/awesome-copilot | 41.0K | +251 | 75 | — |
| ripienaar/free-for-dev | 141.8K | — | 45 | — |
| HenryNdubuaku/maths-cs-ai-compendium | 7.7K | — | 10 | — |
| Category median | 46.9K | +597 | 75 | — |
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