This chapter introduced the distinction between supervised and unsupervised learning. It covered how to use unsupervised learning (such as auto-encoders) to learn the deep or hidden features of data. These hidden features may be used on their own, such as to better understand the structure of data, or for other applications. Two common applications of auto-encoders and unsupervised learning are to identify anomalous data (for example, outlier detection, financial fraud) and to pre-train more complex, often supervised, models such as deep neural networks. In the next chapter, we will learn how to train and build deep neural networks to develop prediction models (that is, supervised learning).
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