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)

Deep Learning Models Using TensorFlow in R

This chapter is about using TensorFlow in R. We have already used TensorFlow quite a lot, as Keras is a high-level neural network API that uses either TensorFlow, CNTK, or Theano. In R, Keras uses TensorFlow in the background. TensorFlow is more difficult to develop deep learning models in. However, there are two interesting packages in TensorFlow that could be overlooked: TensorFlow estimators and TensorFlow runs. We will cover both of these packages in this chapter.

In this chapter, we will cover the following topics:

  • Introduction to TensorFlow
  • Building models using TensorFlow
  • TensorFlow estimators
  • TensorFlow runs packages