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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

Use case – using MXNet for classification and regression

In this section, we will use a new dataset to create a binary classification task. The dataset we will use here is a transactional dataset that is available at https://www.dunnhumby.com/sourcefiles. This dataset has been made available from dunnhumby, which is perhaps best known for its link to the Tesco (a British grocery store) club-card, which is one of the largest retail loyalty systems in the world. I recommend the following book, which describes how dunnhumby helped Tesco to become the number one retailer by applying analytics to their retail loyalty program: Humby, Clive, Terry Hunt, and Tim Phillips. Scoring points. Kogan Page Publishers, 2008. Even though this book is relatively old, it remains one of the best use cases to describe how to roll out a business-transformation program based on data analytics...

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