https://github.com/GPflow/GPflow
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getting_started.rst
Getting Started
===============
This section aims to give you the knowledge necessary to use GPflow on small-to-medium projects,
without necessarily going too much into the mathematical and technical details. We do not try to
teach the theory behind Gaussian Processes.
* For a brief introduction to the mathematics of Gaussian Processes we recommend
`this article <http://www.inference.org.uk/mackay/gpB.pdf>`_.
* For a longer text on the theory of Gaussian Processes we recommend the book:
`Gaussian Processes for Machine Learning <https://gaussianprocess.org/gpml/>`_.
* If you need a deeper understanding of the technical details or advanced features of GPflow please,
see our :doc:`user_guide`.
We will assume you are reasonably familiar with `Python <https://www.python.org/>`_,
`NumPy <https://numpy.org/>`_ and maybe `TensorFlow <https://www.tensorflow.org/>`_, and it is good
if you have some previous experience with data processing or machine learning in Python. We do not
assume prior knowldege of Gaussian Processes.
.. toctree::
:maxdepth: 1
installation
notebooks/getting_started/basic_usage
notebooks/getting_started/kernels
notebooks/getting_started/mean_functions
notebooks/getting_started/parameters_and_their_optimisation
notebooks/getting_started/large_data
notebooks/getting_started/classification_and_other_data_distributions
notebooks/getting_started/monitoring
notebooks/getting_started/saving_and_loading