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)

Convolutional layers

This section shows how convolutional layers work in greater depth. At a basic level, convolutional layers are nothing more than a set of filters. When you look at images while wearing glasses with a red tint, everything appears to have a red hue. Now, imagine if these glasses consisted of different tints embedded within them, maybe a red tint with one or more horizontal green tints. If you had such a pair of glasses, the effect would be to highlight certain aspects of the scene in front of you. Any part of the scene that had a green horizontal line would become more focused.

Convolutional layers apply a selection of patches (or convolutions) over the previous layer’s output. For example, for a face recognition task, the first layer’s patches identify basic features in the image, for example, an edge or a diagonal line. The patches are moved across...