<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transfer Learning on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/transfer-learning/</link><description>Recent content in Transfer Learning on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 17 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/transfer-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 21: Project: Building a Custom Image Classifier</title><link>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/project-image-classifier/</link><pubDate>Sat, 17 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/project-image-classifier/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 21! After exploring the theoretical foundations of deep learning, neural networks, and various architectures, it&amp;rsquo;s time to get your hands dirty with a complete, practical project. In this chapter, we&amp;rsquo;ll build a custom image classifier from scratch, leveraging the power of modern deep learning frameworks and techniques.&lt;/p&gt;
&lt;p&gt;This project will guide you through the entire lifecycle of an image classification task: from preparing your own dataset, to selecting and modifying a pre-trained model, training it, and evaluating its performance. By the end, you&amp;rsquo;ll not only have a working image classifier but also a much deeper understanding of the practical considerations involved in real-world deep learning applications. This is a foundational skill for any aspiring AI/ML engineer or researcher, opening doors to advanced computer vision tasks.&lt;/p&gt;</description></item></channel></rss>