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🧠 Implementations/tutorials of deep learning papers with side-by-side notes; including transformers (original, xl, switch, feedback, vit), optimizers(adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), reinforcement learning (ppo, dqn), capsnet, distillation, etc.

Project description

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labml.ai Deep Learning Paper Implementaion

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.

Screenshot

We are actively maintaining this repo and adding new implementations almost weekly. Twitter for updates.

Modules

Transformers

Recurrent Highway Networks

LSTM

HyperNetworks - HyperLSTM

ResNet

Capsule Networks

Generative Adversarial Networks

Sketch RNN

✨ Graph Neural Networks

Counterfactual Regret Minimization (CFR)

Solving games with incomplete information such as poker with CFR.

Reinforcement Learning

Optimizers

Normalization Layers

Distillation

Adaptive Computation

Installation

pip install labml-nn

Citing

If you use this for academic research, please cite it using the following BibTeX entry.

@misc{labml,
 author = {Varuna Jayasiri, Nipun Wijerathne},
 title = {labml.ai Annotated Paper Implementations},
 year = {2020},
 url = {https://nn.labml.ai/},
}

Project details


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