AI-ML-Cheatsheets is one of those rare repositories that doesn’t ship runnable code but instead delivers a well-curated, structured knowledge base for AI and machine learning practitioners. If you’ve ever found yourself scrambling for quick math formulas or algorithm summaries during study sessions, research, or interviews, this repo offers a portable set of cheatsheets to keep on hand.
What AI-ML-Cheatsheets offers
This repository collects an extensive set of reference materials originating from Stanford and other authoritative sources. It spans the entire AI/ML stack — from foundational topics like linear algebra, calculus, and probability/statistics through classical machine learning algorithms to the latest deep learning techniques, transformer architectures, and large language models (LLMs).
Each topic is organized into its own folder containing concise cheatsheets in PDF and Markdown formats. These sheets provide key formulas, concept explanations, and diagrams designed for quick recall rather than deep tutorials. Because it’s purely documentation, there’s no code, no dependencies, and no build steps. You just clone and open the files offline.
The repo’s audience includes students preparing for exams or interviews, researchers needing quick refreshers, and developers who want a solid reference without hunting down scattered notes or textbooks. Its global reach is reflected in its subscriber count of over 10,000 newsletter followers from 150+ countries.
How the documentation is structured and its practical strengths
The cheatsheets are modular and topic-focused, which makes navigation straightforward. Key folders include:
- Math basics such as linear algebra (vectors, matrices, eigenvalues), calculus (derivatives, integrals), and probability/statistics fundamentals.
- Classical ML algorithms like regression, decision trees, SVMs, clustering, including formulas and high-level explanations.
- Deep learning with math derivations behind neural networks, backpropagation, optimization techniques.
- Transformers and LLMs focusing on attention mechanisms, architecture diagrams, and key properties of models like GPT.
This modular approach allows users to pick exactly the topics they want to review without sifting through a monolithic document. The availability in PDF and Markdown caters to different use cases: PDF for quick offline reading and printing, Markdown for integration into personal notes or modification.
The tradeoff here is obvious: it’s not an interactive tutorial or a coded library. There’s no runnable code or live examples, so users must already have some background to make full use of these cheatsheets. But that’s a deliberate design choice, focusing on clarity and portability over interactivity.
The code quality question doesn’t apply here, but the quality of the documentation is high. The sheets are concise and well-organized, avoiding unnecessary clutter while still covering the essential formulas and concepts. The diagrams are clear and support the textual explanations well.
Quick start
git clone https://github.com/analyticalrohit/AI-ML-Cheatsheets.git
Once cloned, you can browse the folders by topic and open the PDFs or Markdown files in your preferred reader or editor. There is no installation required.
Verdict
AI-ML-Cheatsheets is a practical, no-frills reference collection for anyone working or studying in AI and machine learning who needs quick access to foundational math and algorithmic concepts. It’s especially suited to students, interview candidates, and researchers who want a reliable, offline memory aid.
Its limitation is that it provides no runnable code or interactive learning experience. If you’re looking for tutorials, implementations, or hands-on code, you’ll need to look elsewhere. But as a well-organized repository of cheatsheets that cover a wide breadth of AI/ML topics, it fills a niche that many practitioners appreciate.
Overall, it’s a useful resource worth bookmarking if you often find yourself needing to refresh fundamental concepts or formulas without wading through textbooks or scattered notes.
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→ GitHub Repo: analyticalrohit/AI-ML-Cheatsheets ⭐ 881