#6060th of 88 in Learning and cookbooks
papers-in-100-lines-of-code
Short single-file implementations of 69 machine learning papers
- Stars
- 2.9k
- License
- MIT
- Last commit
- Oct 2026
- Language
- Python
2,711 on 21 Apr 20262,912 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A collection of Python implementations of well-known ML papers, each written in about 100 lines of code. It covers GANs, diffusion models, normalizing flows, deep RL, meta-learning, NeRF variants and 3D Gaussian Splatting, from Maxout Networks (2013) to a 2025 paper. Each entry links to the paper and lists its authors and date.
Who it is for: ML students and engineers learning papers by reading compact code
Strengths
- Covers 69 papers, from GANs and diffusion to NeRF and Gaussian Splatting
- Each implementation is kept to about 100 lines, so it is quick to read
- Every entry links to its source paper with authors and date
- MIT licensed, with recent additions up to a December 2025 paper
Weaknesses
- README lists no install steps, dependencies or run instructions
- Condensed code omits full-scale training setups and pretrained weights
- No tests, releases or reproduced benchmark numbers are mentioned
- Reads as a paper index; there is no explanatory text per implementation
Quick answers
Is papers-in-100-lines-of-code open source?
Yes. Its code is on GitHub under the MIT license.
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