// the take
A grab-bag of deep learning sample code from Eli Bendersky's personal study notes, spanning Coursera/cs231n/Udacity course exercises, from-scratch linear/logistic regression, and more recent transformer/LLM internals (attention, MoE, a llama2 reimplementation, a JAX word2vec). It's for developers who want to see mechanics implemented directly rather than learn a library's API.
The repo traces an honest progression from numpy-level linear regression up through multi-head attention and a llama2-from-scratch implementation, so you can see the same ideas rebuilt at increasing complexity. Several modules ship unit tests next to the implementation (regression_lib_test.py, test_multiheadattention.py, test_moe.py), which is rare discipline for sample code. The transformer-attention and pytorch-samples folders isolate specific mechanisms (attention heads, top-k, MoE routing) in small files instead of burying them in a training loop, which makes them easy to read in isolation.
The top-level README is a single line; there's no index explaining what's in each folder or why, so you're exploring blind. It's not a library or curriculum, it's a pile of unrelated course exercises and one-off scripts accumulated over years, with inconsistent tooling (some folders use uv/pyproject.toml, older ones have no dependency management at all). No single entrypoint or install instructions, so each folder is its own mini-project you have to figure out separately. Last pushed May 2025 with no active issues or discussion, so this reads as a personal reference rather than something maintained for other people to build on.
We read the README, the directory tree and a key source file in October 2026. Nobody pays to be listed. How we judge →