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)

Getting Started with Deep Learning

This chapter discusses deep learning, a powerful multilayered architecture for pattern-recognition, signal-detection, and classification or prediction. Although deep learning is not new, it is only in the past decade that it has gained great popularity, due in part to advances in computational capacity and new ways of more efficiently training models, as well as the availability of ever-increasing amounts of data. In this chapter, you will learn what deep learning is, the R packages available for training such models, and how to get your system set up for analysis. We will briefly discuss MXNet and Keras, which are the two main frameworks that we will use for many of the examples in later chapters to actually train and use deep learning models.

In this chapter, we will explore the following topics:

  • What is deep learning?
  • A conceptual overview of deep learning
  • Setting up your R environment and the deep learning frameworks available in R
  • GPUs and reproducibility