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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning of Representations: Looking Forward · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1305.0445/</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/1305.0445/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning of Representations: Looking Forward — mentioned by dave_sullivan</title><link>https://news.ycombinator.com/item?id=7589048</link><guid isPermaLink="false">urn:hackerpapers:mention:1305.0445:7589048</guid><pubDate>Mon, 14 Apr 2014 21:24:12 GMT</pubDate><description>&lt;p&gt;This book is really coming together. It&amp;#x27;s been a while since I&amp;#x27;ve put together a (100% not comprehensive) list of good places to start if you&amp;#x27;re looking to learn more and/or use deep learning in your projects. Open source Pylearn2 (used to win kaggle galaxies competition): http://deeplearning.net/software/pylearn2/ Theano (symbolic math library used by Pylearn2): http://deeplearning.net/software/theano/ Deep learning tutorials with theano (build your own neural networks): http://www.deeplearning.net/tutorial/ Demos Convnet JS: http://cs.stanford.edu/people/karpathy/convnetjs/ Sentiment…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1305.0445/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
