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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

What is deep learning?

Deep learning (also known by some within the industry as deep structured learning or hierarchical learning, among other titles) is really part of a wider family, or branch, of machine learning methods, as mentioned earlier. These methods are based on learning what is known as representations (that is, where the model discovers from the data the representations, patterns, or rules needed to carry out a desired task or meet an objective), as opposed to task specific algorithms (that is, detailed rules written out or predefined, describing how to perform a specific task).

Note

Representations or feature representations are critical to all types of learning. Feature representations can be learned and predefined manually or defined automatically by the model while analyzing the data.

An alternative to manual instruction

As an alternative to the process of manually creating rules, instructions, or equations deemed essential to solving a problem and then organizing data to be...

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