This repository provides compact implementations of influential machine learning and deep learning research papers, condensed to approximately 100 lines of code each. Implementations span generative adversarial networks (GANs), variational autoencoders (VAE), diffusion models, reinforcement learning algorithms like DQN, and optimization methods like Adam. The project serves as an educational resource for practitioners learning ML concepts through readable, minimal code examples rather than full research codebases.