<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Parameter-Sharing on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/parameter-sharing/</link><description>Recent content in Parameter-Sharing on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 09 Jul 2026 21:02:20 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/parameter-sharing/index.xml" rel="self" type="application/rss+xml"/><item><title>CNN from scratch</title><link>https://abaj.ai/wiki/ml/dl/cnn/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/dl/cnn/</guid><description>&lt;p>a convolutional neural network is a &lt;a
 href="https://abaj.ai/wiki/ml/dl/feedforward/"
 
 
>feedforward network&lt;/a> with its linear layers put on a diet: instead of every unit seeing every input, each unit sees a small local window, and every window is processed by the &lt;em>same&lt;/em> small set of weights.&lt;span class="margin-note" data-note="the licence for this diet is an assumption about images: nearby pixels are related, faraway pixels mostly are not, and a feature worth detecting is worth detecting everywhere">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
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this page builds the operation from its definition, gets the geometry formulas straight, walks the canonical architectures, and ends with a convolution written in loops and checked against scipy (Goodfellow, Ian, 2016).&lt;/p></description></item></channel></rss>