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R Deep Learning Essentials

You're reading from   R Deep Learning Essentials A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Length 378 pages
Edition 2nd Edition
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Authors (2):
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Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

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...

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