<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.1.1">Jekyll</generator><link href="https://pksx01.github.io/AI_Blogs/feed.xml" rel="self" type="application/atom+xml" /><link href="https://pksx01.github.io/AI_Blogs/" rel="alternate" type="text/html" /><updated>2022-04-03T00:56:54-05:00</updated><id>https://pksx01.github.io/AI_Blogs/feed.xml</id><title type="html">Pavan Kumar’s AI Blog</title><subtitle>Here, I talk about AI.</subtitle><entry><title type="html">Bear Classification: From Data Collection to GUI for Model Inference</title><link href="https://pksx01.github.io/AI_Blogs/2022/03/29/bear-classification-using-fastai-2.html" rel="alternate" type="text/html" title="Bear Classification: From Data Collection to GUI for Model Inference" /><published>2022-03-29T00:00:00-05:00</published><updated>2022-03-29T00:00:00-05:00</updated><id>https://pksx01.github.io/AI_Blogs/2022/03/29/bear-classification-using-fastai-2</id><author><name>Pavan Kumar Singh</name></author><summary type="html"><![CDATA[In this notebook, we are going to understand the concepts and codes discussed in Lesson 2 of fast.ai book and it's Deep Learning Part-1 2020 course. We will be classifying bear into 3 categories: grizzly, black and teddy bears.]]></summary></entry></feed>