<?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>激活函数 on ML Learning Notes</title><link>https://omegazhang3.github.io/ml-blog/tags/%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0/</link><description>Recent content in 激活函数 on ML Learning Notes</description><generator>Hugo -- 0.125.7</generator><language>en-us</language><lastBuildDate>Sun, 04 Oct 2026 12:21:03 +0000</lastBuildDate><atom:link href="https://omegazhang3.github.io/ml-blog/tags/%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0/index.xml" rel="self" type="application/rss+xml"/><item><title>深入理解 Softmax 函数：原理、公式与实战</title><link>https://omegazhang3.github.io/ml-blog/posts/softmax/</link><pubDate>Sun, 04 Oct 2026 12:21:03 +0000</pubDate><guid>https://omegazhang3.github.io/ml-blog/posts/softmax/</guid><description>&lt;blockquote>
&lt;p>Softmax 是深度学习中最经典的函数之一，几乎所有多分类模型的输出层都少不了它。本文将从原理、数学公式、代码实现到实际应用，带你彻底搞懂 Softmax。&lt;/p>
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