<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep-Learning on Neural Odyssey</title><link>https://danialjfz.github.io/myblog/tags/deep-learning/</link><description>Recent content in Deep-Learning on Neural Odyssey</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>Danialj999@gmail.com (Danial Jafarzadeh)</managingEditor><webMaster>Danialj999@gmail.com (Danial Jafarzadeh)</webMaster><copyright>© 2026 Danial Jafarzadeh</copyright><lastBuildDate>Thu, 20 Nov 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://danialjfz.github.io/myblog/tags/deep-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Neural Networks: Building Intuition Beyond the Math</title><link>https://danialjfz.github.io/myblog/posts/neural-networks-intuition/</link><pubDate>Thu, 20 Nov 2025 00:00:00 +0000</pubDate><author>Danialj999@gmail.com (Danial Jafarzadeh)</author><guid>https://danialjfz.github.io/myblog/posts/neural-networks-intuition/</guid><description>&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;br&gt;
This piece explains neural networks from the intuition upward: what a neuron is, why layers help, how gradient descent changes weights, and why backpropagation is less mystical than it sounds. It is written for readers who want the concepts to feel concrete before diving deeper into the math.&lt;/p&gt;</description></item></channel></rss>