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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

Introduction to ImageNet

ImageNet is a database of over 15 million hand-labeled, high-resolution images in roughly 22,000 categories. This database is organized just like the WordNet hierarchy, where each concept is also called a synset (that is, synonym set). Each synset is a node in the ImageNet hierarchy. Each node has more than 500 images. 

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) was founded in 2010 to improve state-of-the-art technology for object detection and image classification on a large scale:

Following this overview of ImageNet, we will now take a look at various CNN model architectures.

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