Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

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...