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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Return of the Devil in the Details: Delving Deep into Convolutional Nets · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1405.3531/</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/1405.3531/feed.xml" rel="self" type="application/rss+xml" /><item><title>Return of the Devil in the Details: Delving Deep into Convolutional Nets — mentioned by rsfern</title><link>https://news.ycombinator.com/item?id=34451702</link><guid isPermaLink="false">urn:hackerpapers:mention:1405.3531:34451702</guid><pubDate>Fri, 20 Jan 2023 11:37:16 GMT</pubDate><description>&lt;p&gt;This looks like a great and really comprehensive resource! Karpathy’s recipe for training neural nets is one of the only similar documents I’ve come across http://karpathy.github.io/2019/04/25/recipe/ There’s also the (older) devil in the details papers focused on computer vision. I’d love to read something like this on modern methods like transformers. https://arxiv.org/abs/1405.3531&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1405.3531/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
