Create an account to use AI

Create a free account to unlock all features:

Ask questions about videos with AI
Use smart search (AI)
Save your favorite videos
Bookmark your favorite categories
Organize your favorite subcategories
Access your personalized favorites page
Access your conversation history
Personalize your homepage with your favorites

Create an account

Already have an account? Log in

Lecture 9 - Normalization and Regularization

Deep Learning Systems Course

This lecture gives an overview of normalization layers in deep networks (such as LayerNorm and BatchNorm). It also discusses methods for regularizing networks, include L2 regularization and Dropout. Finally, we cover some challenges with the interaction of optimization, initialization, normalization, and regularization.

Sign up for the course for free at https://dlsyscourse.org.

Contents:

00:00:00 - Introduction

00:01:07 - Intialization vs. optimization

00:12:22 - Normalization

00:13:51 - Layer normalization

00:19:12 - LayerNorm illustration

00:23:05 - Batch normalization

00:27:29 - Minibatch dependence

00:34:03 - Regularization of deep networks

00:36:34 - Regularization

00:40:27 - L2 regularization a.k.a. weight decay

00:53:27 - Caveats of L2 regularization

00:55:38 - Dropout

00:58:55 - Dropout as stochastic approximation

01:04:36 - Many solution ... many more questions

01:06:44 - BatchNorm: An illustrative example

01:12:36 - BatchNorm: Other benefits?

01:15:46 - The ultimate takeaway message

Lecture 9 - Normalization and Regularization | Knoovi