Metadata-Version: 2.1
Name: labml-nn
Version: 0.4.7
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/
Description: [![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)
        * [Cycle GAN](http://lab-ml.com/labml_nn/gan/cycle_gan.html)
        
        #### ✨ [Sketch RNN](http://lab-ml.com/labml_nn/sketch_rnn/)
        
        
        ### 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/},
        }
        ```
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
