<?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 Tristan Rice</title><link>https://fn.lc/tags/machine-learning/</link><description>Recent content in Machine Learning on Tristan Rice</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 08 Jul 2017 00:34:19 -0700</lastBuildDate><atom:link href="https://fn.lc/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>nwHacks Machine Learning</title><link>https://fn.lc/post/nwhacks-ml/</link><pubDate>Sat, 08 Jul 2017 00:34:19 -0700</pubDate><guid>https://fn.lc/post/nwhacks-ml/</guid><description>&lt;p>I&amp;rsquo;ve been doing a bunch of work during my internship with Machine Learning
models so I figured I take a crack at applying them to some of my personal
projects. Just for fun I wanted to see what would happen if I tried to train a
model on the registration, check-in and submission data for nwHacks.&lt;/p>
&lt;p>I decided to use &lt;a href="https://github.com/xlvector/hector">Hector&lt;/a>, a suite of
algorithms completely written in Go since that&amp;rsquo;s what most of the nwHacks
tooling is written in. Took a few hours to write a pipeline that would read in
the nwHacks registration data from Firebase and output it into a format that
Hector supports.&lt;/p></description></item></channel></rss>