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)

Anomaly Detection and Recommendation Systems

This chapter will look at auto-encoder models and recommendation systems. Although these two use cases may seem very different, they both rely on finding different representations of data. These representations are similar to the embeddings we saw in Chapter 7, Natural Language Processing Using Deep Learning. The first part of this chapter introduces unsupervised learning where there is no specific outcome to be predicted. The next section provides a conceptual overview of auto-encoder models in a machine learning and deep neural network context in particular. We will show you how to build and apply an auto-encoder model to identify anomalous data. Such atypical data may be bad data or outliers, but could also be instances that require further investigation, for example, fraud detection. An example of applying anomaly detection is detecting...