So far we have focused on models and techniques that broadly fall under the category of supervised learning. Supervised learning is supervised in the sense that the task is for the machine to learn the relationship between a set of variables or features and one or more outcomes. Often, there is only a single outcome. For example, a company may wish to predict whether someone is likely to become a customer, in which case the outcome of whether an individual becomes a customer coded as yes/no. In this chapter, we will delve into methods of unsupervised learning. In contrast with supervised learning, where there is an outcome variable(s) or labeled data is used, unsupervised learning does not require any outcomes or labeled data. Unsupervised learning uses only input features for learning. A common example of unsupervised learning is cluster analysis, such as K-means clustering, where the machine learns hidden or latent clusters in the data to minimize...
R Deep Learning Essentials
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R Deep Learning Essentials
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Overview of this book
<p>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 the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.</p>
<p>This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.</p>
<p>After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.</p>
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Table of Contents (14 chapters)
R Deep Learning Essentials
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Getting Started with Deep Learning
Training a Prediction Model
Preventing Overfitting
Identifying Anomalous Data
Training Deep Prediction Models
Tuning and Optimizing Models
Bibliography
Index
Customer Reviews