<?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>Autograd on 安橙的博客</title><link>https://blog.ans20xx.com/tags/autograd/</link><description>Recent content in Autograd on 安橙的博客</description><generator>Hugo -- 0.162.1</generator><language>zh</language><lastBuildDate>Wed, 20 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.ans20xx.com/tags/autograd/index.xml" rel="self" type="application/rss+xml"/><item><title>Day 09 · Autograd 原理</title><link>https://blog.ans20xx.com/posts/ai/day09/</link><pubDate>Wed, 20 May 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day09/</guid><description>用 ~200 行 Python 手写一个 mini-autograd:理解动态计算图的构建、链式法则的层层应用、拓扑排序的反向遍历。在最小可运行实现里看清 PyTorch backward() 的真面目。</description></item><item><title>Day 08 · PyTorch 核心抽象</title><link>https://blog.ans20xx.com/posts/ai/day08/</link><pubDate>Tue, 19 May 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day08/</guid><description>打开 PyTorch 黑盒:Tensor 与 Storage 的分离、Dispatcher 的多重派发机制、Autograd Engine 的工作原理。跟踪一行 a + b 从 Python 一路调到 CUDA kernel 的完整路径。</description></item></channel></rss>