<?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>Batch Size on Answer</title>
    <link>https://answer.freetools.me/tags/batch-size/</link>
    <description>Recent content in Batch Size on Answer</description>
    <generator>Hugo -- 0.152.2</generator>
    <language>zh-cn</language>
    <lastBuildDate>Wed, 11 Mar 2026 23:43:13 +0800</lastBuildDate>
    <atom:link href="https://answer.freetools.me/tags/batch-size/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Batch Size的选择：为什么这个超参数能决定模型的生死</title>
      <link>https://answer.freetools.me/batch-size%E7%9A%84%E9%80%89%E6%8B%A9%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E8%B6%85%E5%8F%82%E6%95%B0%E8%83%BD%E5%86%B3%E5%AE%9A%E6%A8%A1%E5%9E%8B%E7%9A%84%E7%94%9F%E6%AD%BB/</link>
      <pubDate>Wed, 11 Mar 2026 23:43:13 +0800</pubDate>
      <guid>https://answer.freetools.me/batch-size%E7%9A%84%E9%80%89%E6%8B%A9%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E8%B6%85%E5%8F%82%E6%95%B0%E8%83%BD%E5%86%B3%E5%AE%9A%E6%A8%A1%E5%9E%8B%E7%9A%84%E7%94%9F%E6%AD%BB/</guid>
      <description>深度解析神经网络训练中batch size选择背后的理论原理，从泛化差距到尖锐最小值，从梯度噪声到学习率缩放，揭示为什么小batch往往比大batch泛化更好。</description>
    </item>
    <item>
      <title>梯度累积真的能模拟大批量训练吗？从数学等价性到隐性成本的完整解析</title>
      <link>https://answer.freetools.me/%E6%A2%AF%E5%BA%A6%E7%B4%AF%E7%A7%AF%E7%9C%9F%E7%9A%84%E8%83%BD%E6%A8%A1%E6%8B%9F%E5%A4%A7%E6%89%B9%E9%87%8F%E8%AE%AD%E7%BB%83%E5%90%97%E4%BB%8E%E6%95%B0%E5%AD%A6%E7%AD%89%E4%BB%B7%E6%80%A7%E5%88%B0%E9%9A%90%E6%80%A7%E6%88%90%E6%9C%AC%E7%9A%84%E5%AE%8C%E6%95%B4%E8%A7%A3%E6%9E%90/</link>
      <pubDate>Wed, 11 Mar 2026 22:27:41 +0800</pubDate>
      <guid>https://answer.freetools.me/%E6%A2%AF%E5%BA%A6%E7%B4%AF%E7%A7%AF%E7%9C%9F%E7%9A%84%E8%83%BD%E6%A8%A1%E6%8B%9F%E5%A4%A7%E6%89%B9%E9%87%8F%E8%AE%AD%E7%BB%83%E5%90%97%E4%BB%8E%E6%95%B0%E5%AD%A6%E7%AD%89%E4%BB%B7%E6%80%A7%E5%88%B0%E9%9A%90%E6%80%A7%E6%88%90%E6%9C%AC%E7%9A%84%E5%AE%8C%E6%95%B4%E8%A7%A3%E6%9E%90/</guid>
      <description>深入解析梯度累积技术的数学原理、正确实现方式与隐性成本。从GPU显存构成分析到损失归一化的细节，从BatchNorm冲突到分布式训练中的性能陷阱，揭示这个被广泛使用的显存优化技术的完整技术图景。</description>
    </item>
  </channel>
</rss>
