<?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>自注意力 on AI 实战派 · 从技术到赚钱</title><link>https://guijiagi.com/tags/%E8%87%AA%E6%B3%A8%E6%84%8F%E5%8A%9B/</link><description>Recent content in 自注意力 on AI 实战派 · 从技术到赚钱</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Wed, 07 Oct 2026 17:48:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E8%87%AA%E6%B3%A8%E6%84%8F%E5%8A%9B/index.xml" rel="self" type="application/rss+xml"/><item><title>论文重读：Attention Is All You Need，Transformer 为什么改写了一切</title><link>https://guijiagi.com/posts/2026-10-07-paper-attention-is-all-you-need/</link><pubDate>Wed, 07 Oct 2026 17:48:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-07-paper-attention-is-all-you-need/</guid><description>2017 年那篇只用注意力、不用循环与卷积的论文。拆解自注意力、多头机制与位置编码，看它为何成为大模型地基。</description></item></channel></rss>