<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RNN on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/rnn/</link><description>Recent content in RNN on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 19 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://arshadhs.github.io/tags/rnn/index.xml" rel="self" type="application/rss+xml"/><item><title>Recurrent Neural Networks</title><link>https://arshadhs.github.io/docs/ai/deep-learning/070-recurrent-nn/</link><pubDate>Sun, 19 Apr 2026 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/deep-learning/070-recurrent-nn/</guid><description>&lt;h1 id="recurrent-neural-networks">
 Recurrent Neural Networks
 
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&lt;p>Recurrent Neural Networks (RNNs) are neural networks designed for &lt;strong>sequential data&lt;/strong>, where the order of inputs matters and the model must use information from earlier time steps to interpret later ones. Unlike a feedforward network, an RNN does not process each input in isolation. It carries a &lt;strong>hidden state&lt;/strong> from one time step to the next, so the network can build a running summary of what it has seen so far.&lt;/p></description></item></channel></rss>