<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dmimno on Journal of Digital Humanities</title><link>https://journalofdigitalhumanities.org/author/dmimno/</link><description>Recent content in Dmimno on Journal of Digital Humanities</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sat, 01 Dec 2012 00:00:00 +0000</lastBuildDate><atom:link href="https://journalofdigitalhumanities.org/author/dmimno/index.xml" rel="self" type="application/rss+xml"/><item><title>The Details: Training and Validating Big Models on Big Data</title><link>https://journalofdigitalhumanities.org/2-1/the-details-by-david-mimno/</link><pubDate>Sat, 01 Dec 2012 00:00:00 +0000</pubDate><guid>https://journalofdigitalhumanities.org/2-1/the-details-by-david-mimno/</guid><description>&lt;p&gt;In this video, David Mimno discusses some of the different choices one can make in training models and what their implications are for efficiency, scalability, and topic quality, using the MALLET topic modeling package. This presentation was recorded on November 3, 2012 at the Maryland Institute for Technology as part of the &lt;a href="https://mith.umd.edu/topicmodeling" title="MITH Workshop, 'Topic Modeling'"&gt;Topic Modeling Workshop&lt;/a&gt;, sponsored by the National Endowment of the Humanities and MITH, at the University of Maryland. Slides are available &lt;a href="http://www.cs.princeton.edu/~mimno/slides/details.pdf" title="David Mimno, Slides for Topic Modeling Presentation"&gt;here&lt;/a&gt;.&lt;/p&gt;</description></item></channel></rss>