<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tutorial on Neural Odyssey</title><link>https://danialjfz.github.io/myblog/tags/tutorial/</link><description>Recent content in Tutorial 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/tutorial/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><item><title>Build Your First ML Model: A No-BS Guide</title><link>https://danialjfz.github.io/myblog/posts/build-your-first-ml-model/</link><pubDate>Tue, 18 Nov 2025 00:00:00 +0000</pubDate><author>Danialj999@gmail.com (Danial Jafarzadeh)</author><guid>https://danialjfz.github.io/myblog/posts/build-your-first-ml-model/</guid><description>&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;br&gt;
This post walks through a first end-to-end ML workflow using a real image classification task. The goal is not just to train a model, but to build the habits that matter in practice: checking data, splitting correctly, choosing a simple baseline, and evaluating results without fooling yourself.&lt;/p&gt;</description></item></channel></rss>