<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>梯度下降 on 111qqz's blog</title><link>https://111qqz.com/en/tags/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/</link><description>Recent content in 梯度下降 on 111qqz's blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2015-2026 111qqz</copyright><lastBuildDate>Mon, 10 Jul 2017 01:49:04 +0000</lastBuildDate><atom:link href="https://111qqz.com/en/tags/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/index.xml" rel="self" type="application/rss+xml"/><item><title>几种梯度下降(GD)法的比较（转载）</title><link>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2017-07-10-gradient-descent-methods/</link><pubDate>Mon, 10 Jul 2017 01:49:04 +0000</pubDate><guid>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2017-07-10-gradient-descent-methods/</guid><description>&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Gradient_descent" target="_blank" rel="noreferrer"&gt;参考资料&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;机器学习中梯度下降（Gradient Descent， GD）算法只需要计算损失函数的一阶导数，计算代价小，非常适合训练数据非常大的应用。&lt;/p&gt;</description></item></channel></rss>