<?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>Machine Learning on Thamme Gowda</title><link>https://gowda.ai/tags/machine-learning/</link><description>Recent content in Machine Learning on Thamme Gowda</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 27 Jun 2026 20:00:00 +0000</lastBuildDate><atom:link href="https://gowda.ai/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>A Field Guide for Sanity-Checking Your Training Pipeline</title><link>https://gowda.ai/posts/2026/06/dropout-overfit-sanity-check/</link><pubDate>Sat, 27 Jun 2026 20:00:00 +0000</pubDate><guid>https://gowda.ai/posts/2026/06/dropout-overfit-sanity-check/</guid><description>A practical guide to the overfit sanity check for ML researchers: use a realistic small set, measure the inference-mode metric, and keep dropout on even when deliberately overfitting.</description></item></channel></rss>