<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tensor Decomposition on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/tensor-decomposition/</link><description>Recent content in Tensor Decomposition on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 26 Mar 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/tensor-decomposition/index.xml" rel="self" type="application/rss+xml"/><item><title>Essence of Linear Algebra (13): Tensors and Multilinear Algebra</title><link>https://www.chenk.top/en/linear-algebra/13-tensors-and-multilinear-algebra/</link><pubDate>Wed, 26 Mar 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/linear-algebra/13-tensors-and-multilinear-algebra/</guid><description>&lt;p>If you&amp;rsquo;ve used PyTorch or TensorFlow, you&amp;rsquo;ve met the word &amp;ldquo;tensor&amp;rdquo; hundreds of times. PyTorch calls every array &lt;code>torch.Tensor&lt;/code>; TensorFlow puts it in the product name. But what &lt;em>is&lt;/em> a tensor, and why did frameworks borrow this physics-flavored word for what looks like a multi-dimensional array?&lt;/p>
&lt;p>The short answer from this chapter:&lt;/p>
&lt;blockquote>
&lt;p>A tensor is the natural generalization of a scalar, vector, and matrix to &lt;strong>arbitrary&lt;/strong> dimensions. Everything you know about matrices either lifts cleanly to tensors, or breaks in instructive ways.&lt;/p></description></item></channel></rss>