docs/index.rst
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
########## Disclaimer ##########
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!