mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-08-17 03:14:55 +08:00
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
"""
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# [LabML Neural Networks](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](https://github.com/lab-ml/nn) are documented with explanations,
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and the [website](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.
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## Modules
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#### ✨ [Transformers](transformers/index.html)
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* [Multi-headed attention](transformers/mha.html)
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* [Transformer building blocks](transformers/models.html)
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* [Transformer XL](transformers/xl/index.html)
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* [Relative multi-headed attention](transformers/xl/relative_mha.html)
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* [GPT Architecture](transformers/gpt/index.html)
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* [GLU Variants](transformers/glu_variants/simple.html)
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* [kNN-LM: Generalization through Memorization](transformers/knn/index.html)
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* [Feedback Transformer](transformers/feedback/index.html)
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* [Switch Transformer](transformers/switch/index.html)
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#### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html)
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#### ✨ [LSTM](lstm/index.html)
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#### ✨ [HyperNetworks - HyperLSTM](hypernetworks/hyper_lstm.html)
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#### ✨ [Capsule Networks](capsule_networks/index.html)
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#### ✨ [Generative Adversarial Networks](gan/index.html)
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* [GAN with a multi-layer perceptron](gan/simple_mnist_experiment.html)
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* [GAN with deep convolutional network](gan/dcgan.html)
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* [Cycle GAN](gan/cycle_gan.html)
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#### ✨ [Sketch RNN](sketch_rnn/index.html)
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#### ✨ [Reinforcement Learning](rl/index.html)
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* [Proximal Policy Optimization](rl/ppo/index.html) with
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[Generalized Advantage Estimation](rl/ppo/gae.html)
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* [Deep Q Networks](rl/dqn/index.html) with
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with [Dueling Network](rl/dqn/model.html),
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[Prioritized Replay](rl/dqn/replay_buffer.html)
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and Double Q Network.
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#### ✨ [Optimizers](optimizers/index.html)
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* [Adam](optimizers/adam.html)
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* [AMSGrad](optimizers/amsgrad.html)
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* [Adam Optimizer with warmup](optimizers/adam_warmup.html)
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* [Noam Optimizer](optimizers/noam.html)
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* [Rectified Adam Optimizer](optimizers/radam.html)
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* [AdaBelief Optimizer](optimizers/ada_belief.html)
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#### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
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* [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
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* [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.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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"""
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