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82 lines
3.3 KiB
Markdown
82 lines
3.3 KiB
Markdown
[](https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/)
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[](https://twitter.com/LabML1)
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# [LabML Neural Networks](https://lab-ml.com/labml_nn/index.html)
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This is a collection of simple PyTorch implementations of
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neural networks and related algorithms.
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These implementations are documented with explanations,
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[The website](https://lab-ml.com/labml_nn/index.html)
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renders these as side-by-side formatted notes.
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We believe these would help you understand these algorithms better.
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We are actively maintaining this repo and adding new
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implementations almost weekly.
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[Follow us on Twitter](https://twitter.com/LabML1) for updates.
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## Modules
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#### ✨ [Transformers](https://lab-ml.com/labml_nn/transformers)
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[Transformers module](https://lab-ml.com/labml_nn/transformers)
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contains implementations for
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[multi-headed attention](https://lab-ml.com/labml_nn/transformers/mha.html)
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and
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[relative multi-headed attention](https://lab-ml.com/labml_nn/transformers/relative_mha.html).
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* [kNN-LM: Generalization through Memorization](https://lab-ml.com/labml_nn/transformers/knn)
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* [Feedback Transformer](https://lab-ml.com/labml_nn/transformers/feedback)
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#### ✨ [Recurrent Highway Networks](https://lab-ml.com/labml_nn/recurrent_highway_networks)
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#### ✨ [LSTM](https://lab-ml.com/labml_nn/lstm)
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#### ✨ [HyperNetworks - HyperLSTM](https://lab-ml.com/labml_nn/hypernetworks/hyper_lstm.html)
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#### ✨ [Capsule Networks](https://lab-ml.com/labml_nn/capsule_networks/)
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#### ✨ [Generative Adversarial Networks](https://lab-ml.com/labml_nn/gan/)
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* [GAN with a multi-layer perceptron](https://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
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* [GAN with deep convolutional network](https://lab-ml.com/labml_nn/gan/dcgan.html)
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* [Cycle GAN](https://lab-ml.com/labml_nn/gan/cycle_gan.html)
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#### ✨ [Sketch RNN](https://lab-ml.com/labml_nn/sketch_rnn/)
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#### ✨ [Reinforcement Learning](https://lab-ml.com/labml_nn/rl/)
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* [Proximal Policy Optimization](https://lab-ml.com/labml_nn/rl/ppo/) with
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[Generalized Advantage Estimation](https://lab-ml.com/labml_nn/rl/ppo/gae.html)
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* [Deep Q Networks](https://lab-ml.com/labml_nn/rl/dqn/) with
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with [Dueling Network](https://lab-ml.com/labml_nn/rl/dqn/model.html),
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[Prioritized Replay](https://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
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and Double Q Network.
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#### ✨ [Optimizers](https://lab-ml.com/labml_nn/optimizers/)
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* [Adam](https://lab-ml.com/labml_nn/optimizers/adam.html)
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* [AMSGrad](https://lab-ml.com/labml_nn/optimizers/amsgrad.html)
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* [Adam Optimizer with warmup](https://lab-ml.com/labml_nn/optimizers/adam_warmup.html)
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* [Noam Optimizer](https://lab-ml.com/labml_nn/optimizers/noam.html)
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* [Rectified Adam Optimizer](https://lab-ml.com/labml_nn/optimizers/radam.html)
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* [AdaBelief Optimizer](https://lab-ml.com/labml_nn/optimizers/ada_belief.html)
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### Installation
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```bash
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pip install labml_nn
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```
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### Citing LabML
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If you use LabML for academic research, please cite the library using the following BibTeX entry.
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```bibtex
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@misc{labml,
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author = {Varuna Jayasiri, Nipun Wijerathne},
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title = {LabML: A library to organize machine learning experiments},
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year = {2020},
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url = {https://lab-ml.com/},
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}
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```
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