Batch Normalization in PyTorch - Adding Normalization to Convolutional Neural Network Layers
deeplizard via YouTube
Google, IBM & Microsoft Certificates — All in One Plan
AI Engineer - Learn how to integrate AI into software applications
Overview
Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Learn how to implement batch normalization in convolutional neural networks using PyTorch. Explore the concept of batch normalization, create two CNNs using nn.Sequential, prepare the training set, and inject networks into a testing framework. Compare the performance of networks with and without batch normalization, troubleshoot TensorBoard errors, and understand the importance of collective intelligence in deep learning. Gain practical insights into improving neural network training and performance through this hands-on tutorial.
Syllabus
Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources
What is Batch Norm?
Creating Two CNNs Using nn.Sequential
Preparing the Training Set
Injecting Networks Into Our Testing Framework
Running the Tests - BatchNorm vs. NoBatchNorm
Dealing with Error Caused by TensorBoard
Collective Intelligence and the DEEPLIZARD HIVEMIND
Taught by
deeplizard