<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Thumbs up? Sentiment Classification using Machine Learning Techniques · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/cs_0205070/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/cs_0205070/feed.xml" rel="self" type="application/rss+xml" /><item><title>Thumbs up? Sentiment Classification using Machine Learning Techniques — mentioned by digisth</title><link>https://news.ycombinator.com/item?id=10676455</link><guid isPermaLink="false">urn:hackerpapers:mention:cs_0205070:10676455</guid><pubDate>Fri, 04 Dec 2015 14:27:27 GMT</pubDate><description>&lt;p&gt;The below resources are the ones I used when I started to learn about DL/NNs. Some of them are focused specifically on certain applications, but I found them helpful, too. Basic NNs: http://www.wildml.com/2015/09/implementing-a-neural-network-... (a whole series, all worth reading) https://gist.github.com/sthware/c47824c116e6a61a56d9 (my code based on the above) http://iamtrask.github.io/2015/07/27/python-network-part2/ http://rolisz.ro/2013/04/18/neural-networks-in-python/ ML: http://cs229.stanford.edu/materials.html http://onlinestatbook.com/2/index.html DL in general, RNNs, RNTS, CNNs…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/cs_0205070/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
