This repository provides over 60 PyTorch implementations of deep learning papers, targeting researchers and engineers seeking to understand and experiment with state-of-the-art models. It offers side-by-side annotated explanations, facilitating a deeper grasp of complex algorithms.

How It Works

The project implements papers in PyTorch, focusing on clarity and educational value. Each implementation is accompanied by detailed, side-by-side notes that explain the underlying concepts, mathematical formulations, and architectural choices. This approach aims to demystify complex deep learning papers by providing both functional code and pedagogical explanations.

Quick Start & Requirements

• Install via pip: pip install labml-nn
• Requires PyTorch.
• Official documentation and website: https://labml.ai/

Highlighted Details

• Comprehensive coverage of Transformers (original, XL, ViT, etc.), GANs (StyleGAN2, CycleGAN), Diffusion Models (DDPM, Stable Diffusion), and Reinforcement Learning (PPO, DQN).
• Includes implementations for various optimizers (Adam, AdaBelief, Sophia) and normalization techniques.
• Features advanced topics like Capsule Networks, Distillation, and Uncertainty Quantification.
• Actively maintained with new implementations added weekly.

Maintenance & Community

• Actively maintained by labml.ai.
• Frequent updates and additions of new paper implementations.

Licensing & Compatibility

• MIT License.
• Permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

The repository focuses on clear, simple implementations; highly optimized or production-ready code may require further adaptation. Some advanced features or specific paper nuances might be simplified for clarity.