<?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>Python on Joshua Antony | Tech Blog</title><link>https://blogs.joshuaantony.com/tags/python/</link><description>Recent content in Python on Joshua Antony | Tech Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 10 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blogs.joshuaantony.com/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>PyTorch Essentials Cheat Sheet: From Zero to Backpropagation</title><link>https://blogs.joshuaantony.com/posts/pytorch-essentials-cheat-sheet/</link><pubDate>Tue, 10 Mar 2026 00:00:00 +0000</pubDate><guid>https://blogs.joshuaantony.com/posts/pytorch-essentials-cheat-sheet/</guid><description>A dense, correct reference covering tensors, GPU acceleration, autograd, backpropagation, and training loops. Everything you need to understand how PyTorch trains models.</description></item></channel></rss>