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R Deep Learning Projects

You're reading from R Deep Learning Projects Master the techniques to design and develop neural network models in R

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788478403

Length 258 pages

Edition 1st Edition

Languages

R

Tools

Keras

Concepts

Deep Learning

Authors (2):

Yuxi (Hayden) Liu

Pablo Maldonado

View More author details

Table of Contents (7) Chapters

Preface

1. Handwritten Digit Recognition Using Convolutional Neural Networks

2. Traffic Sign Recognition for Intelligent Vehicles FREE CHAPTER

3. Fraud Detection with Autoencoders

4. Text Generation Using Recurrent Neural Networks

5. Sentiment Analysis with Word Embeddings

6. Other Books You May Enjoy

Leave a review - let other readers know what you think

Text fraud detection

Fraud has become an issue beyond the traditional transaction fraud. Many websites, for instance, rely on user reviews about services, such as restaurants, hotels or tourist attractions, that are monetized in different ways. If the users lose trust in those reviews, for example, by a business owner deliberately messing with the good reviews for his or her own business, then the website will find it hard to regain that trust and to remain profitable. Hence, it is important to detect such potential issues.

How can autoencoders help us with this? As before, the idea is to learn the representation of a normal review on a website, and then find those that do not fit the normal review. The issue with text data is that there is some processing to be done before. We will illustrate this with an example, which will also serve as a motivation for the different...

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