labmlai/annotated_deep_learning_paper_implementations

This repository is a collection of over 60 deep learning algorithm implementations, each written in Python with PyTorch and accompanied by detailed inline explanations. The core purpose is educational: rather than just providing working code, every implementation is annotated side-by-side with notes that explain what each piece of the code is doing and why, connecting the code directly to the concepts described in academic research papers. A companion website renders these as formatted documents where the code and explanations appear in parallel columns. The algorithms covered span a broad range of modern deep learning research. There are many implementations of Transformer architectures, the technology underlying large language models, including the original attention mechanism, GPT architecture, Vision Transformers, and specialized variants like Switch Transformer and Flash Attention. The collection also includes generative models (Stable Diffusion, CycleGAN, StyleGAN2), reinforcement learning algorithms (Proximal Policy Optimization, Deep Q Networks), optimization algorithms (Adam, AdaBelief, Sophia), normalization techniques, low-rank adaptation (LoRA) for fine-tuning large models, graph neural networks, and more. Each implementation is clean and readable, deliberately simple rather than production-optimized, so the structure of the algorithm stays visible. This makes it a reference for understanding how a paper's math maps to actual code, not just a library to drop into a project. You would use this repository when studying deep learning research, learning how a specific algorithm actually works at the implementation level, or cross-referencing an academic paper against working code. It is aimed at students, researchers, and engineers who want to go deeper than tutorial blog posts. The stack is Python and PyTorch, installed via pip as the labml-nn package.