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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

Deep learning – a not-so-deep overview

So, what is this deep learning that is grabbing our attention and headlines? Let's turn to Wikipedia again to form a working definition: Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple nonlinear transformations. That sounds as if a lawyer wrote it. The characteristics of deep learning are that it is based on ANNs where the machine learning techniques, primarily unsupervised learning, are used to create new features from the input variables. We will dig into some unsupervised learning techniques in the next couple of chapters, but you can think of it as finding structure in data where no response variable is available.

A simple way to think of it is the periodic...

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