Metadata-Version: 2.1
Name: labml-nn
Version: 0.4.6
Summary: A collection of PyTorch implementations of neural network architectures and layers.
Home-page: https://github.com/lab-ml/labml_nn
Author: Varuna Jayasiri, Nipun Wijerathne
Author-email: vpjayasiri@gmail.com, hnipun@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://lab-ml.com/
Keywords: machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Requires-Dist: labml (>=0.4.41)
Requires-Dist: labml-helpers
Requires-Dist: torch

[![PiPy Version](https://badge.fury.io/py/labml-nn.svg)](https://badge.fury.io/py/labml-nn)
[![PiPy Downloads](https://pepy.tech/badge/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.

**If you have any suggestions for other new implementations,
please create a [Github Issue](https://github.com/lab-ml/labml_nn/issues).**

#### ✨ [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>).

#### ✨ [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)

### Installation

```bash
pip install labml_nn
```

### Links

[💬 Slack workspace for discussions](https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/)_

[📗 Documentation](http://lab-ml.com)

[📑 Articles & Tutorials](https://medium.com/@labml/)

[👨‍🏫 Samples](https://github.com/lab-ml/samples)


### 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/},
}
```

