So, what is this deep learning that is grabbing our attention and headlines? Let's turn to Wikipedia again for 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 one 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 Table of Elements**, which is a classic case of finding structure where no response is specified. Pull up this table online and you...

#### Mastering Machine Learning with R, Second Edition - Second Edition

#### Mastering Machine Learning with R, Second Edition - Second Edition

#### Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.
You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.
With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

Table of Contents (23 chapters)

Title Page

Credits

About the Author

About the Reviewers

Packt Upsell

Customer Feedback

Preface

Free Chapter

A Process for Success

Linear Regression - The Blocking and Tackling of Machine Learning

Logistic Regression and Discriminant Analysis

Advanced Feature Selection in Linear Models

More Classification Techniques - K-Nearest Neighbors and Support Vector Machines

Classification and Regression Trees

Neural Networks and Deep Learning

Cluster Analysis

Principal Components Analysis

Market Basket Analysis, Recommendation Engines, and Sequential Analysis

Creating Ensembles and Multiclass Classification

Time Series and Causality

Text Mining

R on the Cloud

R Fundamentals

Sources

Customer Reviews