Search icon CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
R Deep Learning Essentials. - Second Edition

You're reading from  R Deep Learning Essentials. - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Pages 378 pages
Edition 2nd Edition
Languages
Authors (2):
Mark Hodnett Mark Hodnett
Profile icon Mark Hodnett
Joshua F. Wiley Joshua F. Wiley
Profile icon Joshua F. Wiley
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Getting Started with Deep Learning 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime}