Varuna Jayasiri 299cd650cd github link
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.. image:: https://badge.fury.io/py/labml-nn.svg
    :target: https://badge.fury.io/py/labml-nn
.. image:: https://pepy.tech/badge/labml-nn
    :target: https://pepy.tech/project/labml-nn

LabML Neural Networks
=====================

This is a collection of simple PyTorch implementation of various neural network architectures and layers. We will keep adding to this.

Transformers
------------

`Transformers module  <https://github.com/lab-ml/labml_nn/tree/master/labml_nn/transformers>`_ contains implementations for
`multi-headed attention <https://github.com/lab-ml/labml_nn/blob/master/labml_nn/transformers/mha.py>`_
and
`relative multi-headed attention <https://github.com/lab-ml/labml_nn/blob/master/labml_nn/transformers/relative_mha.py>`_.


Installation
------------

.. code-block:: console

    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.

.. code-block:: bibtex

	@misc{labml,
	 author = {Varuna Jayasiri, Nipun Wijerathne},
	 title = {LabML: A library to organize machine learning experiments},
	 year = {2020},
	 url = {https://lab-ml.com/},
	}

Description
🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Readme 152 MiB
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Python 89.4%
Jupyter Notebook 10.5%
Makefile 0.1%