// the find
afshinea/stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
A collection of PDF cheatsheets covering CNNs, RNNs, and general deep learning tips and tricks, originally produced for Stanford's CS 230 course. It's aimed at students or engineers who want a dense, single-page reference to jog their memory on core deep learning concepts rather than a tutorial or working codebase.
The content is genuinely well-organized and dense — the 'super cheatsheet' compiles all three topics into one reference that's actually useful to skim before an interview or exam. Translated into seven languages including Persian, Japanese, Korean, and Vietnamese, which is unusual reach for an academic reference. Same authors as the well-known CS 229 (ML) cheatsheets, so the format and rigor are consistent with a known-good product.
There is no code here at all — it's PDFs and README files, so it doesn't belong next to repos that ship libraries or tools. Last pushed in 2020, and deep learning practice (transformers, diffusion, modern training tricks) has moved substantially since then, so the 'tips and tricks' sheet in particular is dated. No source files for the cheatsheets themselves (e.g. LaTeX), so you can't fork it and adapt the content, only read the finished PDFs.