<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vectors on Fernando Ruiz</title><link>https://www.fernandoux.com/tags/vectors/</link><description>Recent content in Vectors on Fernando Ruiz</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://www.fernandoux.com/tags/vectors/index.xml" rel="self" type="application/rss+xml"/><item><title>Embeddings</title><link>https://www.fernandoux.com/en/wiki/ai/embeddings/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://www.fernandoux.com/en/wiki/ai/embeddings/</guid><description>&lt;div class="info-panel"&gt;
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 &lt;span class="info-panel-label"&gt;Quick Definition&lt;/span&gt;
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 An &lt;strong&gt;embedding&lt;/strong&gt; is a numerical representation of an item, such as a text, an image or a user, as a vector (a list of numbers). It is designed so that items with similar meaning sit close to each other in that numerical space.
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&lt;h2 id="what-is-an-embedding"&gt;What is an embedding?&lt;/h2&gt;
&lt;p&gt;An embedding turns something complex into a list of numbers. The list is learned from data: a model adjusts its values so that items appearing in similar contexts receive nearby vectors. It is the idea John Rupert Firth summed up in 1957: a word is characterized by the company it keeps.&lt;/p&gt;</description></item></channel></rss>