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R Machine Learning Projects

You're reading from  R Machine Learning Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Profile icon Dr. Sunil Kumar Chinnamgari

Table of Contents (12) Chapters

Preface 1. Exploring the Machine Learning Landscape 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
11. Other Books You May Enjoy

Summary

In this chapter, we learned about an unsupervised deep learning technique called AEs. We covered the definition, working principle, types, and applications of AEs. H2O, an open source library that enables us to create deep learning models, including AEs, was explored. We then discussed a credit card fraud open dataset and implemented a project with an AE to detect fraudulent credit card transactions.

Can deep neural networks help with creative tasks such as prose generation, story writing, caption generation for images, and poem writing? Not sure?! Let's explore RNNs, in the next chapter, a special type of deep neural network that enables us to accomplish creative tasks. Turn the page to explore the world of RNNs for prose generation.

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