A collection of PyTorch implementations of neural network architectures and layers.
Project description
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
- 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/},
}
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