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)

Document classification

This chapter will be looking at text classification using Keras. The dataset we will use is included in the Keras library. As we have done in previous chapters, we will use traditional machine learning techniques to create a benchmark before applying a deep learning algorithm. The reason for this is to show how deep learning models perform against other techniques.

The Reuters dataset

We will use the Reuters dataset, which can be accessed through a function in the Keras library. This dataset has 11,228 records with 46 categories. To see more information about this dataset, run the following code:

library(keras)
?dataset_reuters

Although the Reuters dataset can be accessed from Keras, it is not in a format...