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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 <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>`_.
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, ... 🧠
attentiondeep-learningdeep-learning-tutorialganliterate-programmingloramachine-learningneural-networksoptimizerspytorchreinforcement-learningtransformertransformers
Readme
154 MiB
Languages
Python
89.6%
Jupyter Notebook
10.3%
Makefile
0.1%