Jupyter Notebook
🧠 Minimal implementations/tutorials of deep learning papers with side-by-side notes

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

• GAN with a multi-layer perceptron
• GAN with deep convolutional network
• Cycle GAN

✨ 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

• Adam
• AMSGrad
• Adam Optimizer with warmup
• Noam Optimizer
• Rectified Adam Optimizer
• AdaBelief Optimizer

✨ Normalization Layers

• Batch Normalization

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/},
}