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)

Data augmentation

One approach to increasing the accuracy in a model regardless of the amount of data you have is to create artificial examples based on existing data. This is called data augmentation. Data augmentation can also be used at test time to improve prediction accuracy.

Using data augmentation to increase the training data

We are going to apply data augmentation to the MNIST dataset that we used in previous chapters. The code for this section is in Chapter6/explore.Rmd if you want to follow along. In Chapter 5, Image Classification Using Convolutional Neural Networks, we plotted some examples from the MNIST data, so we won't repeat the code again. It is included in the code file, and you can also refer back...