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R Machine Learning Projects

You're reading from   R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Length 334 pages
Edition 1st Edition
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Author (1):
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Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Author Profile Icon Dr. Sunil Kumar Chinnamgari
Dr. Sunil Kumar Chinnamgari
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Table of Contents (12) Chapters Close

Preface 1. Exploring the Machine Learning Landscape FREE CHAPTER 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

The major theme of this chapter was generating text automatically using RNNs. We started the chapter with a discussion about language models and their applications in the real world. We then carried out an in-depth overview of recurrent neural networks and their suitability for language model tasks. The differences between traditional feedforward networks and RNNs were discussed to get a clearer understanding of RNNs. We then went on to discuss problems and solutions related to the exploding gradients and vanishing gradients experienced by RNNs. After acquiring a detailed theoretical foundation of RNNs, we went ahead with implementing a character-level language model with an RNN. We used Alice's Adventures in Wonderland as a text corpus input to train the RNN model and then generated a string as output. Finally, we discussed some ideas for improving our character...

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