https://www.chenk.top/en/tags/matrix-theory/Recent content in Matrix Theory on Chen Kai BlogHugoenWed, 21 Jan 2026 09:00:00 +0000https://www.chenk.top/en/ml-math-derivations/02-linear-algebra-and-matrix-theory/Wed, 21 Jan 2026 09:00:00 +0000https://www.chenk.top/en/ml-math-derivations/02-linear-algebra-and-matrix-theory/<p><figure class="article-figure"> <img src="https://blog-pic-ck.oss-cn-beijing.aliyuncs.com/posts/en/ml-math-derivations/02-Linear-Algebra-and-Matrix-Theory/illustration_1.png" alt="ML Math Derivations (2): Linear Algebra and Matrix Theory — Chapter overview" loading="lazy" decoding="async" class="content-image"> </figure> </p> <hr> <h2 id="why-this-chapter-and-whats-different" class="heading-anchor">Why this chapter, and what&rsquo;s different<a href="#why-this-chapter-and-whats-different" class="heading-link" aria-label="Permalink to this section" title="Copy link to this section">#</a> </h2><p>If you have already worked through a standard linear-algebra course you have seen most of these objects. <strong>This chapter is not that course.</strong> It is the <em>ML practitioner&rsquo;s slice</em> of linear algebra: the half-dozen ideas that actually appear when you implement gradient descent, run PCA, train a neural net, or read a paper.</p>