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Convolutional Neural Networks 

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Convolutional Neural Networks
 at 
Great Learning 
Overview

Total fee

Free

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Convolutional Neural Networks
 at 
Great Learning 
Highlights

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Convolutional Neural Networks
 at 
Great Learning 
Course details

What are the course deliverables?
  • Convolutional Neural Networks (CNN)
  • Convolution
  • Pooling
  • Batch Normalization
  • Regularization and Normalization in BN
  • Side Effects
More about this course
  • Machine learning and deep neural networks are of increasing interest to engineers.
  • In this course, we will talk about digital images, the convolution process, and pooling features such as max and average pooling
  • We will also uncover kernels and various filters along with feature maps in the Convolution process of CNN

Convolutional Neural Networks
 at 
Great Learning 
Curriculum

Digital Images Overview

Image as a Function

Edge as a Feature

Digital Noise

Convolution Process

Introduction to Pooling

CNN Theoretical Concepts

Data Augmentation

Weight Initialization

Regularization and Dropout

Demo on CNNs

What is Batch Normalization

Introduction to Convolution Process of CNN

Introduction of Batch Normalization

How does Batch Normalization work?

When and how to use Batch Normalization?

How to evaluate Batch Normalization results?

Regularization and Normalization in Batch Normalization

Why is this Method so important?

What is the side effects of Batch Normalization?

Advantages of using Batch Normalization

Summary of Batch Normalization

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Convolutional Neural Networks
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