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Convolutional Neural Networks 

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Convolutional Neural Networks
 at 
UDEMY 
Overview

Gain a comprehensive overview of the Neural Networks principles and concepts

Duration

3 hours

Total fee

Free

Mode of learning

Online

Official Website

Explore Free Course External Link Icon

Credential

Certificate

Convolutional Neural Networks
 at 
UDEMY 
Highlights

  • Earn a certificate of completion from Great Learning
  • Get Free lifetime access
Details Icon

Convolutional Neural Networks
 at 
UDEMY 
Course details

More about this course
  • In this course, we will learn how CNNs work and some of the applications they have been used in
  • 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
  • We will discuss in this course Batch normalization, which is part of Deep Learning

Convolutional Neural Networks
 at 
UDEMY 
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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