<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Representation Learning on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/representation-learning/</link><description>Recent content in Representation Learning on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/representation-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Weaving Information: Data Fusion Strategies</title><link>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/weaving-information-data-fusion-strategies/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/weaving-information-data-fusion-strategies/</guid><description>&lt;h2 id="introduction-the-art-of-combination"&gt;Introduction: The Art of Combination&lt;/h2&gt;
&lt;p&gt;Welcome back, fellow AI explorer! In our previous chapters, we embarked on a fascinating journey, learning how to process individual modalities like text, images, audio, and video, transforming them into meaningful numerical representations, or &lt;em&gt;embeddings&lt;/em&gt;. We saw how powerful these individual encoders can be, but here&amp;rsquo;s a thought: what if we could combine these different perspectives? What if an AI could not just &lt;em&gt;see&lt;/em&gt; an image, but also &lt;em&gt;read&lt;/em&gt; its caption, &lt;em&gt;hear&lt;/em&gt; the accompanying audio, and &lt;em&gt;understand&lt;/em&gt; the context of a video clip, all at once?&lt;/p&gt;</description></item></channel></rss>