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Practical Convolutional Neural Networks

You're reading from  Practical Convolutional Neural Networks

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Pages 218 pages
Edition 1st Edition
Languages
Authors (3):
Mohit Sewak Mohit Sewak
Profile icon Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Profile icon Pradeep Pujari
View More author details

Table of Contents (11) Chapters

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

History of CNNs

There have been numerous attempts to recognize pictures by machines for decades. It is a challenge to mimic the visual recognition system of the human brain in a computer. Human vision is the hardest to mimic and most complex sensory cognitive system of the brain. We will not discuss biological neurons here, that is, the primary visual cortex, but rather focus on artificial neurons. Objects in the physical world are three dimensional, whereas pictures of those objects are two dimensional. In this book, we will introduce neural networks without appealing to brain analogies. In 1963, computer scientist Larry Roberts, who is also known as the father of computer vision, described the possibility of extracting 3D geometrical information from 2D perspective views of blocks in his research dissertation titled BLOCK WORLD. This was the first breakthrough...

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