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R Deep Learning Essentials. - Second Edition

You're reading from  R Deep Learning Essentials. - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Pages 378 pages
Edition 2nd Edition
Languages
Authors (2):
Mark Hodnett Mark Hodnett
Profile icon Mark Hodnett
Joshua F. Wiley Joshua F. Wiley
Profile icon Joshua F. Wiley
View More author details

Table of Contents (13) Chapters

Preface 1. Getting Started with Deep Learning 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Image Classification Using Convolutional Neural Networks

It is not an exaggeration to say that the huge growth of interest in deep learning can be mostly attributed to convolutional neural networks. Convolutional neural networks (CNNs) are the main building blocks of image classification models in deep learning, and have replaced most techniques that were previously used by specialists in the field. Deep learning models are now the de facto method to perform all large-scale image tasks, including image classification, object detection, detecting artificially generated images, and even attributing text descriptions to images. In this chapter, we will look at some of these techniques.

Why are CNNs so important? To explain why, we can look at the history of the ImageNet competition. The ImageNet competition is an open large-scale image classification challenge that has one thousand...

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