<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Normalization on felladog</title><link>https://utsavdarlami.github.io/blogs/tags/normalization/</link><description>Recent content in Normalization on felladog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Sun, 03 Mar 2024 21:33:07 +0545</lastBuildDate><atom:link href="https://utsavdarlami.github.io/blogs/tags/normalization/index.xml" rel="self" type="application/rss+xml"/><item><title>Batch Normalization</title><link>https://utsavdarlami.github.io/blogs/notes/2021-03-06--02-21-33z--batch_normalization/</link><pubDate>Sat, 06 Mar 2021 08:06:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/notes/2021-03-06--02-21-33z--batch_normalization/</guid><description>Acknowledgement : d2l.ai batch normalization L11.2 How BatchNorm Works - Sebastian Raschka https://twitter.com/karpathy/status/1013245864570073090 To read: Why is batch norm becoming so unpopular Questions : What is Normalization? Normalization A popular and effective technique that constantly accelerates the convergence of deep network. Normalizes hidden layer inputs. Helps with exploding/vanishing gradient problems. Can increase the training stability and convergence rate. Can be understood as additional(normalization) layers(with additional parameters). How is it done Batch normalization is applied to individual layers (optionally, to all of them) and works as follows:</description></item></channel></rss>