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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pen and Paper Exercises in Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.13446/</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/2206.13446/feed.xml" rel="self" type="application/rss+xml" /><item><title>Pen and Paper Exercises in Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=43440267</link><guid isPermaLink="false">urn:hackerpapers:submission:2206.13446:43440267</guid><pubDate>Fri, 21 Mar 2025 20:07:12 GMT</pubDate><description>&lt;p&gt;Pen and Paper Exercises in Machine Learning (2022)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;413 points; 58 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2206.13446/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Pen and Paper Exercises in Machine Learning — mentioned by ivan_ah</title><link>https://news.ycombinator.com/item?id=31916103</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.13446:31916103</guid><pubDate>Wed, 29 Jun 2022 03:21:37 GMT</pubDate><description>&lt;p&gt;&amp;gt; section 1.6 about the power method Yeah 1.6 is a really cool exercise! https://arxiv.org/pdf/2206.13446.pdf#page=19 It&amp;#x27;s so cool to see of why this works (as an engineer I learned about power method with handwaving explanation &amp;quot;it works in the limit&amp;quot; but I never knew why it works). So what do we do if we want u_2, the eigenvector that corresponds to lambda_2 ? Math overflow says we can just subtract the u_1 subspace from A [1] then repeat, but would that be numerically stable? (i.e. will that work with floats?) [1] https://math.stackexchange.com/questions/1114777/approximate...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.13446/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Pen and Paper Exercises in Machine Learning — mentioned by sedatk</title><link>https://news.ycombinator.com/item?id=31914687</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.13446:31914687</guid><pubDate>Tue, 28 Jun 2022 23:57:58 GMT</pubDate><description>&lt;p&gt;Direct link to PDF: https://arxiv.org/pdf/2206.13446.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.13446/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Pen and Paper Exercises in Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=31913057</link><guid isPermaLink="false">urn:hackerpapers:submission:2206.13446:31913057</guid><pubDate>Tue, 28 Jun 2022 21:11:16 GMT</pubDate><description>&lt;p&gt;Pen and paper exercises in machine learning (2021)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;371 points; 55 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2206.13446/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
