<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Reproducibility - 标签 - mywebsite</title><link>https://steven-yl.github.io/mywebsite/tags/reproducibility/</link><description>Reproducibility - 标签 - mywebsite</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>steven@gmail.com (Steven)</managingEditor><webMaster>steven@gmail.com (Steven)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Thu, 12 Mar 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://steven-yl.github.io/mywebsite/tags/reproducibility/" rel="self" type="application/rss+xml"/><item><title>PyTorch 随机种子（Seed）全面解释</title><link>https://steven-yl.github.io/mywebsite/seed/</link><pubDate>Thu, 12 Mar 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/seed/</guid><description>系统梳理随机种子的概念与 PyTorch 实践，包括 DataLoader worker、cuDNN、不确定性算子和常见陷阱，帮助你搭建可复现实验环境。</description></item></channel></rss>