Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 4. 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