<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Regression on DataThrillz</title><link>https://datathrillz.com/tags/regression/</link><description>Recent content in Regression on DataThrillz</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 05 May 2020 23:41:00 +0000</lastBuildDate><atom:link href="https://datathrillz.com/tags/regression/index.xml" rel="self" type="application/rss+xml"/><item><title>Regression &amp; Classification Models in PyTorch</title><link>https://datathrillz.com/posts/2020-05-05-regression-classification-models-in-pytorch/</link><pubDate>Tue, 05 May 2020 23:41:00 +0000</pubDate><guid>https://datathrillz.com/posts/2020-05-05-regression-classification-models-in-pytorch/</guid><description>&lt;p&gt;The purpose of this article is to share what I learned from a recent PyTorch course. We will share general machine learning tips as well as insights specific to deep learning library PyTorch.&lt;/p&gt;
&lt;p&gt;PyTorch is a deep learning library for Python and was created by Facebook in 2016. PyTorch is good for deep learning beginners. There are several other popular deep learning frameworks such as TensorFlow, Keras, Chainer, and ONNX. TensorFlow was developed by Google and now includes Keras (previously a separate framework). I chose to deepen my knowledge in PyTorch because it is easy to learn and is commonly used for deep learning. Fastai is another deep learning open source library that is really awesome and intuitive to use. While using Fastai, I found myself curious about the inner workings so it&amp;rsquo;s a plus that fastai wraps PyTorch as I can gain a better understanding of both libraries at once. Two birds, one stone!&lt;/p&gt;</description></item></channel></rss>