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<section id="getting-started">
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading">#</a></h1>
<p>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.</p>
<ul class="simple">
<li><p>For a brief introduction to the mathematics of Gaussian Processes we recommend
<a class="reference external" href="http://www.inference.org.uk/mackay/gpB.pdf">this article</a>.</p></li>
<li><p>For a longer text on the theory of Gaussian Processes we recommend the book:
<a class="reference external" href="https://gaussianprocess.org/gpml/">Gaussian Processes for Machine Learning</a>.</p></li>
<li><p>If you need a deeper understanding of the technical details or advanced features of GPflow please,
see our <a class="reference internal" href="user_guide.html"><span class="doc">User Guide</span></a>.</p></li>
</ul>
<p>We will assume you are reasonably familiar with <a class="reference external" href="https://www.python.org/">Python</a>,
<a class="reference external" href="https://numpy.org/">NumPy</a> and maybe <a class="reference external" href="https://www.tensorflow.org/">TensorFlow</a>, 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.</p>
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<li class="toctree-l1"><a class="reference internal" href="notebooks/getting_started/mean_functions.html">Mean Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks/getting_started/parameters_and_their_optimisation.html">Parameters and Their Optimisation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notebooks/getting_started/saving_and_loading.html">Saving and Loading Models</a></li>
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