Papers-in-100-Lines-of-Code

Why we included this project

Reading a research paper is easier when you can run the idea, and this repo gives you that. It distills over sixty influential machine learning papers into PyTorch implementations that stay close to a hundred lines each, covering a wide arc from early GANs and variational autoencoders to NeRFs, diffusion models, and reinforcement learning agents. Every entry links back to the arXiv source, so you can read the math and then see it turned into runnable code without wading through a dependency forest. The compact size makes it a practical study companion for graduate students and self-taught engineers, and it also works as a starting point for adapting an architecture to your own problem rather than starting from a heavyweight codebase.