<?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>线性代数 on s-ai-unix's Blog</title><link>https://s-ai-unix.github.io/tags/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0/</link><description>Recent content in 线性代数 on s-ai-unix's Blog</description><generator>Hugo -- 0.161.1</generator><language>zh-cn</language><lastBuildDate>Thu, 29 Jan 2026 08:00:00 +0800</lastBuildDate><atom:link href="https://s-ai-unix.github.io/tags/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0/index.xml" rel="self" type="application/rss+xml"/><item><title>张量：从数学抽象到深度学习核心的系统综述</title><link>https://s-ai-unix.github.io/posts/2026-01-29-tensor-comprehensive-guide/</link><pubDate>Thu, 29 Jan 2026 08:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-29-tensor-comprehensive-guide/</guid><description>深入浅出解析张量的数学原理与广泛应用，从张量代数到深度学习，从物理场论到数据分析，完整呈现张量的力量</description></item><item><title>谱定理：线性代数的优雅与机器学习的基石</title><link>https://s-ai-unix.github.io/posts/2026-01-25-spectral-theorem/</link><pubDate>Sun, 25 Jan 2026 18:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-25-spectral-theorem/</guid><description>从对称矩阵到深度学习：系统性介绍谱定理的核心理论及其在机器学习中的应用，包括正交对角化、SVD、PCA、谱聚类和图神经网络</description></item></channel></rss>