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External Resources, Videos and Talks
===========================================

For written documentation, see the :ref:`user guide <user_guide>`. 

New to Scientific Python?
==========================
For those that are still new to the scientific Python ecosystem, we highly
recommend the `Python Scientific Lecture Notes
<http://scipy-lectures.github.io/>`_. This will help you find your footing a
bit and will definitely improve your sklearn-theano experience.  A basic
understanding of NumPy arrays is recommended to make the most of scikit-learn.

A decent grasp of `Theano <http://deeplearning.net/software/theano>`_
is also useful for developers who wish to build their own modules
or contribute.

.. _videos:

Videos
======

- `Neural Networks for Computer Vision
  <https://speakerdeck.com/kastnerkyle/euroscipy2014>`_ by `Kyle Kastner`_ at
  EuroScipy2014

    A thirty minute talk describing the use of Python tools for
    computer vision, and introducing some of the core examples and usage
    of sklearn-theano.

.. _Kyle Kastner: http://kastnerkyle.github.io