LabML Neural Networks
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,
The website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
We are actively maintaining this repo and adding new implementations almost weekly. for updates.
Modules
Transformers
✨Transformers module contains implementations for multi-headed attention and relative multi-headed attention.
- GPT Architecture
- GLU Variants
- kNN-LM: Generalization through Memorization
- Feedback Transformer
- Switch Transformer
Recurrent Highway Networks
✨LSTM
✨HyperNetworks - HyperLSTM
✨Capsule Networks
✨Generative Adversarial Networks
✨Sketch RNN
✨Reinforcement Learning
✨- Proximal Policy Optimization with Generalized Advantage Estimation
- Deep Q Networks with with Dueling Network, Prioritized Replay and Double Q Network.
Optimizers
✨Installation
pip install labml_nn
Citing LabML
If you use LabML for academic research, please cite the library using the following BibTeX entry.
@misc{labml,
author = {Varuna Jayasiri, Nipun Wijerathne},
title = {LabML: A library to organize machine learning experiments},
year = {2020},
url = {https://lab-ml.com/},
}