MaximeVandegar/Papers-in-100-Lines-of-Code

What it solves

This project provides concise, minimal implementations of influential AI and machine learning research papers, aiming to make complex theoretical concepts more accessible by limiting each implementation to approximately 100 lines of code.

How it works

The repository serves as a collection of standalone scripts that implement the core logic of various research papers. It covers a wide range of architectures and techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Neural Radiance Fields (NeRFs), and Diffusion Models, translating the mathematical descriptions in the papers into compact, readable code.

Who it’s for

It is designed for developers and researchers who want to understand the inner workings of AI models by reading a simplified version of the core algorithm rather than navigating through large, professional production-grade libraries.

Highlights

• Over 60 implemented papers including foundational work like GANs, Adam optimizer, and Stable Diffusion v1-5.
• Focuses on extreme brevity (100 lines of code) to facilitate rapid learning.
• Covers diverse domains including image synthesis, 3D scene representation (NeRFs), and reinforcement learning.