<?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>Sgd on The Standard Error</title><link>https://jongha-jeon-dev.github.io/standarderror/tags/sgd/</link><description>Recent content in Sgd on The Standard Error</description><generator>Hugo -- 0.123.7</generator><language>en-gb</language><lastBuildDate>Tue, 13 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jongha-jeon-dev.github.io/standarderror/tags/sgd/index.xml" rel="self" type="application/rss+xml"/><item><title>ML 22: Learning Rate and Batch Size Are One Knob, Until the Rate Hits a Wall</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-22-lr-batch/</link><pubDate>Tue, 13 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-22-lr-batch/</guid><description>With plain SGD and a fixed number of examples, multiplying the batch size and the learning rate by the same factor gives the same training run, until the learning rate reaches the point where training fails.</description></item></channel></rss>