<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Double-Descent on The Standard Error</title><link>https://jongha-jeon-dev.github.io/standarderror/tags/double-descent/</link><description>Recent content in Double-Descent on The Standard Error</description><generator>Hugo -- 0.123.7</generator><language>en-gb</language><lastBuildDate>Thu, 08 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jongha-jeon-dev.github.io/standarderror/tags/double-descent/index.xml" rel="self" type="application/rss+xml"/><item><title>ML 10: The Second Descent Is Real, and a Ridge Penalty Makes It Unnecessary</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-10-double-descent/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-10-double-descent/</guid><description>Past the point where a model fits its training data exactly, test error falls again. Measured on random features, the fall is real, never reaches the best small model, and disappears as a reason once the fit is penalised.</description></item></channel></rss>