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<!DOCTYPE html>
<html>
<head>
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<script type="text/javascript" src='https://ajax.googleapis.com/ajax/libs/jquery/1.10.1/jquery.min.js'></script>
<link rel="stylesheet" href="html/CSS/home.css">
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<style>
.back-text-color { /* color of text on the back of each card */
color: #333;
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<ul>
<li class="menu-item active"><a href="index.html"><span class="menu-item">HOME</span></a></li>
<li class="menu-item"><a href="html/pages/presentations.html"><span class="menu-item">PRESENTATIONS</span></a></li>
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<br/><br/>
<p id="title">Machine Learning Refined</p>
<p style="padding: 0px 240px 0px 240px; text-align: justify;" id="sub-title">
This is a blog about machine learning / deep learning fundamentals built by the authors of the textbook <a class="redlink" target="_blank" href="http://mlrefined.com">Machine Learning Refined</a> published by Cambridge University Press.
The posts - cut into short series - use careful writing and interactive coding widgets to provide an intuitive and playful way to learn about core concepts in AI- from some of the most basic to the most advanced.
<br>
<br>
Each and every post here is a Python Jupyter notebook - prettied up for the web - that you can download and run on your own machine by pulling <a class="redlink" target="_blank" href="https://github.com/jermwatt/mlrefined"> our repo</a>.
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<div class="wrapper">
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<p>REINFORCEMENT LEARNING</p>
</div>
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<div class="box2">
<p><code class="back-text-color">Topics include basic Q-learning algorithm and common enhancements, deep reinforcement learning, and more.</code></p>
<p><code><a href="html/pages/topics/Reinforcement_Learning.html">click to read</a></code></p>
</div>
</div>
</div>
</div>
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<p>MATHEMATICAL OPTIMIZATION</p>
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<p><code class="back-text-color"> Topics here include methods by which we determine proper parameters for machine learning and deep learning models.</code></p>
<p><code><a href="html/pages/topics/Mathematical_Optimization.html">click to read</a></code></p>
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<p>LINEAR SUPERVISED LEARNING</p>
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<p><code class="back-text-color">Topics include linear regression, logistic regression, support vector machines, and more.</code></p>
<p><code><a href="html/pages/topics/Linear_Supervised_Learning.html">click to read</a></code></p>
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</div>
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<p>UNSUPERVISED LEARNING</p>
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<p><code class="back-text-color">Topics include K-means clustering, spectral clustering, PCA, recommender systems, non-negative matrix factorization, and more. </code></p>
<p><code><a href="html/pages/topics/Unsupervised_Learning.html">click to read</a></code></p>
</div>
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</div>
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<p>COMPUTATIONAL LINEAR ALGEBRA</p>
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<p><code class="back-text-color">In this series we review topics from linear algebra that are foundational to understanding machine learning. </code></p>
<p><code><a href="html/pages/topics/Computational_Linear_Algebra.html">click to read</a></code></p>
</div>
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<p>COMPUTATIONAL CALCULUS</p>
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<div class="back">
<div class="box2">
<p><code class="back-text-color">A series of posts on the pivotal role Calculus plays in machine learning.</code></p>
<p><code><a href="html/pages/topics/Computational_Calculus.html">click to read</a></code></p>
</div>
</div>
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<p>COMPUTATIONAL MATHEMATICS</p>
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<br/>
<p><code class="back-text-color">A refresher on mathematical functions and how they are used in machine learning.</code></p>
<p><code><a href="html/pages/topics/Computational_Mathematics.html">click to read</a></code></p>
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</div>
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