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<h1>Another Datum</h1>
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<p>Here you'll find resources about data science, software engineering, and life. Well, maybe not life...</p>
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<a href="http://anotherdatum.com/vae2.html" rel="bookmark" title="Permalink to Variational Autoencoders Explained in Detail">
<h2 class="post-title">
Variational Autoencoders Explained in Detail
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<p class="article-summary">
Learn all the details needed to implement a variational autoencoder, code included.
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<p class="post-meta">Posted on 11 November 2018</p>
<p>There are <a data-disqus-identifier="vae2.html" href="http://anotherdatum.com/vae2.html#disqus_thread">comments</a>.</p> </div>
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<a href="http://anotherdatum.com/embark.html" rel="bookmark" title="Permalink to How to Engineer Your Way Out of Slow Models">
<h2 class="post-title">
How to Engineer Your Way Out of Slow Models
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So you just finished designing that great neural network architecture. But how do you handle the fact it is slow?
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<p class="post-meta">Posted on 28 October 2018</p>
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<h2 class="post-title">
Zooming Past the Competition
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How to create an Augmented Reality app that allows a user to get content recommendations.
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<p class="post-meta">Posted on 10 October 2018</p>
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Variational Autoencoders Explained
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<p class="article-summary">
Ever wondered how the Variational Autoencoder model works? Keep reading to find out.
</p>
<p class="post-meta">Posted on 14 September 2018</p>
<p>There are <a data-disqus-identifier="vae.html" href="http://anotherdatum.com/vae.html#disqus_thread">comments</a>.</p> </div>
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Uncertainty for CTR Prediction: One Model to Clarify Them All
</h2>
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<p class="article-summary">
Learn how to handle uncertainty in recommender systems in a principled way using one unified model.
</p>
<p class="post-meta">Posted on 21 August 2018</p>
<p>There are <a data-disqus-identifier="ctr-model.html" href="http://anotherdatum.com/ctr-model.html#disqus_thread">comments</a>.</p> </div>
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<h2 class="post-title">
Recommender Systems: Exploring the Unknown Using Uncertainty
</h2>
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<p class="article-summary">
Learn what the exploration-exploitation tradeoff is, and how to use your model's uncertainty to explore new items in a wise manner.
</p>
<p class="post-meta">Posted on 14 August 2018</p>
<p>There are <a data-disqus-identifier="exploration-exploitation.html" href="http://anotherdatum.com/exploration-exploitation.html#disqus_thread">comments</a>.</p> </div>
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<h2 class="post-title">
Neural Networks from a Bayesian Perspective
</h2>
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<p class="article-summary">
Learn how to estimate model uncertainty in neural networks.
</p>
<p class="post-meta">Posted on 06 August 2018</p>
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<h2 class="post-title">
Using Uncertainty to Interpret your Model
</h2>
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<p class="article-summary">
Interpreting deep learning models is hard. Learn how to use uncertainty estimates to get insights about what the model has learned.
</p>
<p class="post-meta">Posted on 31 July 2018</p>
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<h2 class="post-title">
Neural Networks gone wild! They can sample from discrete distributions now!
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Learn how to use Gumbel distribution to form a NN containing a discrete random component.
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<p class="post-meta">Posted on 16 July 2018</p>
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Deep Learning: Theory & Practice
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Summary of TCE conference - "Deep Learning: Theory & Practice".
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<p class="post-meta">Posted on 18 June 2018</p>
<p>There are <a data-disqus-identifier="tce_2018.html" href="http://anotherdatum.com/tce_2018.html#disqus_thread">comments</a>.</p> </div>
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