<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Machine-Learning on BradCypert.com</title>
    <link>https://www.bradcypert.com/tags/machine-learning/</link>
    <description>Recent content in Machine-Learning on BradCypert.com</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Tue, 27 Dec 2022 22:25:45 -0500</lastBuildDate>
    <atom:link href="https://www.bradcypert.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>KNN: My Nearest Neighbors</title>
      <link>https://www.bradcypert.com/k-nearest-neighbors/</link>
      <pubDate>Sat, 03 Jun 2017 00:00:00 +0000</pubDate>
      <guid>https://www.bradcypert.com/k-nearest-neighbors/</guid>
      <description>&lt;p&gt;Thanks for tuning in for another &lt;em&gt;fantastic&lt;/em&gt; &lt;strong&gt;awesome&lt;/strong&gt; &lt;code&gt;hardcore&lt;/code&gt; &lt;del&gt;slippery&lt;/del&gt; blog post! In this post, we’re going to cover KNN and it’s implementation in Clojure!&lt;/p&gt;&#xA;&lt;h4 id=&#34;what-is-knn&#34;&gt;What is KNN?&lt;/h4&gt;&#xA;&lt;p&gt;KNN (K-Nearest Neighbors) is simply an algorithm, but you probably knew that at this point. For many, KNN is a terrifying first step into a domain that they’re often not too familiar with — machine learning. That being said, KNN gets looped into several much more complex things by categorizing it like so. Specifically, they get elevated to the same complexity of Neural Networks. KNN is not a neural network, and in fact is simply a classification algorithm, similar conceptually to Voronoi diagrams or even Bayes classifiers.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
