README.html

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<li><a href="#neural-networks-reading-group">Neural Networks Reading Group</a><ul>
<li><a href="#what-the-neural-network-reading-group-is-about">What the Neural Network Reading group is about</a></li>
<li><a href="#website-for-the-neural-network-reading-group">Website for the Neural Network Reading Group</a></li>
<li><a href="#additional-information-on-the-neural-network-reading-group">Additional Information on the Neural Network Reading Group</a></li>
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<div id="neural-networks-reading-group" class="section level1">
<h1>Neural Networks Reading Group</h1>
<div id="what-the-neural-network-reading-group-is-about" class="section level2">
<h2>What the Neural Network Reading group is about</h2>
<p>Repository hosting website for the Neural Networks reading group at the University of Bristol. The website will allow memebers of the Neural Network reading group to give reading suggestions, learn about the time and location of the Neural Networks reading group, and see more general updates.</p>
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<div id="website-for-the-neural-network-reading-group" class="section level2">
<h2>Website for the Neural Network Reading Group</h2>
<p>The Neural Networks website is available at <a href="https://neuralnetworksbristol.netlify.com/" class="uri">https://neuralnetworksbristol.netlify.com/</a></p>
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<div id="additional-information-on-the-neural-network-reading-group" class="section level2">
<h2>Additional Information on the Neural Network Reading Group</h2>
<p>The rest of this readme is structured in a way in which hopefully will make this repo turn up in Google Searches when someone will be looking for Neural Networks reading group at the University of Bristol. The reading group in the school of mathematics at the university of bristol is co-organized by Mauro Camara Escudero and Pierre-Aurelien Gilliot. The main topic will be neural networks and their applications in computational statistics. This means that papers relating neural networks usage in computational statistics and data science will be analized. For instance, we have explored variational auto-encoders which use neural networks to parametrize probability distributions for a latent variable model.</p>
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