Welcome to numpy-ml
===================
`numpy-ml`_ is a growing collection of machine learning models, algorithms, and
tools written exclusively in `NumPy`_ and the Python `standard library`_.

The purpose of the project is to provide reference implementations of common
machine learning components for rapid prototyping and experimentation. With
that in mind, don't just read the docs -- read the source!

.. _numpy-ml: https://www.github.com/ddbourgin/numpy-ml
.. _NumPy: https://numpy.org/
.. _standard library: https://docs.python.org/3/library/

.. topic:: This documentation is under development!

    We're working to expand our coverage. During this time there are likely to
    be typos, bugs, and poorly-worded sections. If you encounter any of the
    above, please file an `issue`_ or submit a `pull request`_!

.. _issue: https://github.com/ddbourgin/numpy-ml/issues
.. _pull request: https://github.com/ddbourgin/numpy-ml/pulls

.. toctree::
   :maxdepth: 3
   :hidden:

   numpy_ml.hmm

   numpy_ml.gmm

   numpy_ml.lda

   numpy_ml.ngram

   numpy_ml.bandits

   numpy_ml.rl_models

   numpy_ml.nonparametric

   numpy_ml.factorization

   numpy_ml.trees

   numpy_ml.neural_nets

   numpy_ml.linear_models

   numpy_ml.preprocessing

   numpy_ml.utils

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Disclaimer
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This software is provided as-is: there are no guarantees that it fits your
purposes or that it is bug-free. Use it at your own risk!