mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-28 12:45:07 +08:00
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
"""
|
|
[](https://badge.fury.io/py/labml-nn)
|
|
[](https://pepy.tech/project/labml-nn)
|
|
|
|
# [LabML Neural Networks](http://lab-ml.com/labml_nn/index.html)
|
|
|
|
This is a collection of simple PyTorch implementation of various
|
|
neural network architectures and layers.
|
|
We will keep adding to this.
|
|
|
|
## Modules
|
|
|
|
#### ✨ [Transformers](http://lab-ml.com/labml_nn/transformers)
|
|
|
|
[Transformers module](http://lab-ml.com/labml_nn/transformers)
|
|
contains implementations for
|
|
[multi-headed attention](http://lab-ml.com/labml_nn/transformers/mha.html)
|
|
and
|
|
[relative multi-headed attention](http://lab-ml.com/labml_nn/transformers/relative_mha.html).
|
|
|
|
* [kNN-LM: Generalization through Memorization](http://lab-ml.com/labml_nn/transformers/knn)
|
|
|
|
#### ✨ [Recurrent Highway Networks](http://lab-ml.com/labml_nn/recurrent_highway_networks)
|
|
|
|
#### ✨ [LSTM](http://lab-ml.com/labml_nn/lstm)
|
|
|
|
#### ✨ [Capsule Networks](http://lab-ml.com/labml_nn/capsule_networks/)
|
|
|
|
#### ✨ [Generative Adversarial Networks](http://lab-ml.com/labml_nn/gan/)
|
|
* [GAN with a multi-layer perceptron](http://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
|
|
* [GAN with deep convolutional network](http://lab-ml.com/labml_nn/gan/dcgan.html)
|
|
* [Cycle GAN](http://lab-ml.com/labml_nn/gan/cycle_gan.html)
|
|
|
|
#### ✨ [Sketch RNN](http://lab-ml.com/labml_nn/sketch_rnn/)
|
|
|
|
#### ✨ [Reinforcement Learning](http://lab-ml.com/labml_nn/rl/)
|
|
* [Proximal Policy Optimization](http://lab-ml.com/labml_nn/rl/ppo/) with
|
|
[Generalized Advantage Estimation](http://lab-ml.com/labml_nn/rl/ppo/gae.html)
|
|
* [Deep Q Networks](http://lab-ml.com/labml_nn/rl/dqn/) with
|
|
with [Dueling Network](http://lab-ml.com/labml_nn/rl/dqn/model.html),
|
|
[Prioritized Replay](http://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
|
|
and Double Q Network.
|
|
|
|
### Installation
|
|
|
|
```bash
|
|
pip install labml_nn
|
|
```
|
|
|
|
### Citing LabML
|
|
|
|
If you use LabML for academic research, please cite the library using the following BibTeX entry.
|
|
|
|
```bibtex
|
|
@misc{labml,
|
|
author = {Varuna Jayasiri, Nipun Wijerathne},
|
|
title = {LabML: A library to organize machine learning experiments},
|
|
year = {2020},
|
|
url = {https://lab-ml.com/},
|
|
}
|
|
```
|
|
"""
|