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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benefits of depth in neural networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1602.04485/</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/1602.04485/feed.xml" rel="self" type="application/rss+xml" /><item><title>Benefits of depth in neural networks — mentioned by j2kun</title><link>https://news.ycombinator.com/item?id=11449064</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.04485:11449064</guid><pubDate>Thu, 07 Apr 2016 17:39:26 GMT</pubDate><description>&lt;p&gt;I spent a few weeks closely reading this book and I have to disagree with the majority here. I didn&amp;#x27;t like the book at all. And I am an advanced math geek. My main issue is that the book tells you all about the different parameter tweaks, but passes little concrete wisdom to the reader. It doesn&amp;#x27;t distinguish between modeling assumptions, and it replaces very simple explanations of concepts with complicated paragraphs that I can&amp;#x27;t make sense of. I think it boils down to something that I have been feeling and hearing a lot in the past few years: the statistical jargon is so overwhelming that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.04485/"&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>
