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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Deep Neural Networks


The last type of technique that we will be discussing before wrapping up our chapter is deep neural networks or deep learning. This is a long and complicated topic, which by no means will we be able to do justice in a short section of this chapter. A complete book may not even suffice to cover the surface of the topic! We will explore the topic from 100 feet and quickly study an easy implementation in R.

Deep neural networks, which are primarily used in the field of computer vision and natural language processing, have also found significance in machine learning use cases for regression and classification on tabular cross-sectional data. With large amounts of data, deep neural networks have been proved to be very effective at learning latent patterns and thus training models with better performance.

A Deeper Look into Deep Neural Networks

Deep neural networks were inspired by the neural structure of the human brain. The field of deep learning became popular for solving...

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