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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

An example of deep learning

Shifting gears away from the Space Shuttle, let's work through how to set up, train, and evaluate a deep learning model. You see these used quite a bit for image classification, NLP, and so on. However, let's look at using it for regression. You don't find too many examples of that in my opinion. As such, let's go with our Ames housing price data we used back in Chapter 2, Linear Regression. Before that, let's briefly discuss what Tensor, TensorFlow, and Keras are.

Keras and TensorFlow background

I mentioned earlier that Keras is an API, a frontend if you will, for several deep learning backends. It was originally available only for Python but has been available in R since...

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