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)

Training Deep Prediction Models

The previous chapters covered a bit of the theory behind neural networks and used some neural network packages in R. Now it is time to dive in and look at training deep learning models. In this chapter, we will explore how to train and build feedforward neural networks, which are the most common type of deep learning model. We will use MXNet to build deep learning models to perform classification and regression using a retail dataset.

This chapter will cover the following topics:

  • Getting started with deep feedforward neural networks
  • Common activation functions rectifiers, hyperbolic tangent, and maxout
  • Introduction to the MXNet deep learning library
  • Use case Using MXNet for classification and regression