All the formulas you forgot since sophomore year, in one repo

What it does This repository bundles quick-reference guides for artificial intelligence, transformers, deep learning, machine learning, probability, statistics, algebra, and calculus. The README bills them as “quick-reference stanford guides,” though it never clarifies whether they are official course materials or simply inspired by them. Either way, the goal is rapid recall: concise explanations, diagrams, and essential equations organized into topic folders.

The interesting bit The scope is deliberately full-stack. You get LLM architectures and neural-network theory packed into the same repo as the undergraduate math—linear algebra, calculus, and probability—that actually powers them. It treats the prerequisites as part of the toolkit rather than a separate chore.

Key highlights

• Covers AI, transformers, deep learning, machine learning, and foundational math topics in one place.
• Organized by folder with diagrams and equations for quick browsing.
• Explicitly aimed at students, developers, and researchers who need to jog their memory.
• Has accumulated 925+ stars, suggesting plenty of people want their math compressed to a single page.

Caveats

• Provenance is hazy: the “stanford guides” label appears in the README without evidence or elaboration on origin.
• No rendered index or search; finding a specific formula means manually browsing folder trees.
• The README offers no previews of the actual sheet contents, so you must clone or navigate the repo blind to assess quality.

Verdict A handy bookmark for students and interview-crammers who need memory joggers on backprop, Bayes, or attention mechanisms. Skip it if you want executable code, interactive tutorials, or vetted primary-source coursework.