<?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>Useful Work</title><link>https://sinitame.github.io/</link><description>Recent content on Useful Work</description><image><title>Useful Work</title><url>https://sinitame.github.io/images/og.png</url><link>https://sinitame.github.io/images/og.png</link></image><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 17 Jun 2020 00:00:00 +0200</lastBuildDate><atom:link href="https://sinitame.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>An introduction to Variational Auto Encoders (VAEs)</title><link>https://sinitame.github.io/posts/an-introduction-to-variational-auto-encoders-vaes/</link><pubDate>Wed, 17 Jun 2020 00:00:00 +0200</pubDate><guid>https://sinitame.github.io/posts/an-introduction-to-variational-auto-encoders-vaes/</guid><description>Understanding Variational Autoencoders (VAEs) from theory to practice using PyTorch</description></item><item><title>Understanding Batch Normalization for Neural Networks</title><link>https://sinitame.github.io/posts/understanding-batch-normalization-for-neural-networks/</link><pubDate>Sun, 01 Mar 2020 00:00:00 +0100</pubDate><guid>https://sinitame.github.io/posts/understanding-batch-normalization-for-neural-networks/</guid><description>Discover how batch normalization can make your deep learning models learn better and how to implement it from scratch in PyTorch.</description></item></channel></rss>