labml.ai Deep Learning Paper Implementations
PyTorch implementations of many deep learning papers, with side-by-side explanatory notes.labmlai/annotated_deep_learning_paper_implementations
- Stars
- 67.6k
- Forks
- 6.8k
- Licence
- MIT
- Language
- Python
- Last update
- 4 days ago
- Type
- Learning resource
What it is
This is a collection of simple PyTorch implementations of neural networks and related algorithms, covering topics such as transformers, GANs, diffusion models, optimizers, and reinforcement learning. Each implementation is documented with explanations rendered as side-by-side notes on the project website. It is meant to help readers understand how these algorithms work.
- Read the annotated Transformer or attention implementation to understand how it works
- Run an individual model such as DQN or PPO to see a reinforcement learning example
- Study the optimizer implementations, such as Adam or AdaBelief, step by step
- Browse the website notes for a paper before reading the original publication
Great for
- Students learning how transformers, GANs, or optimizers work internally
- Developers who already know PyTorch and want readable reference code for a specific paper
- Researchers wanting a quick, simplified starting point to compare against a paper
- Readers who prefer code with written explanations alongside it
Not ideal for
- People wanting a production-ready library, since the README describes these as simple implementations meant for understanding
- Anyone needing a complete, pretrained model they can use without writing code
- Readers without basic Python or deep learning background, as the material assumes familiarity with the topics
How to get it
- 1
Install with a package manager
You'll need: Python · PyTorch
Install the labml-nn package
pip install labml-nn - 2
Run it
- Install PyTorch and Python if you do not already have them
- Run pip install labml-nn as shown in the README
- Open the website at https://nn.labml.ai to read the annotated notes for a topic
- Use the linked source files to run or modify an implementation of interest
- 3
Get the most out of it
- Read the side-by-side notes on the website alongside the code to follow each paper's logic
- Start with the Transformer section, which the README lists in the most depth
- Explore the reinforcement learning section, which includes PPO and DQN with prioritized replay
- Use the Annotated Implementation Skill mentioned in the README with coding agents to implement other papers
Downloads
Strengths
- Each implementation is paired with written explanations and a side-by-side presentation on the website
- Covers a wide range of topics, from transformers and diffusion models to reinforcement learning and optimizers
- Released under the MIT license
Trade-offs
- The README describes implementations as simple, so they may omit features needed for real-world training at scale
- The README is truncated and does not detail per-model setup, so running specific examples may require reading the website or source
- The README notes that some sections are updated almost weekly, so coverage and file layout may change
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| annotated_deep_learning_paper_implementations (this) | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
| minGPT | 25k | Library | intermediate | 2.2 years ago | Students who want to read a working GPT implementation end to end |
| LaTeX-OCR | 16.6k | AI model / agent | intermediate | 1.8 years ago | Students who want to turn a handwritten or printed formula photo into editable LaTeX |
| onnxruntime | 22.2k | Library | intermediate | 4 days ago | Developers who want to deploy a trained model inside an application or service |
| wandb | 11.3k | Library | beginner | 4 days ago | Machine learning engineers who want to compare training runs over time |
| petals | 10.7k | Library | intermediate | 2.2 years ago | Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.