This chapter discusses deep learning, a powerful multi-layered architecture for pattern recognition, signal detection, and classification or prediction. Although deep learning is not new, it is only in the past decade that it has gained great popularity, due in part to advances in computational capacity and new ways of more efficiently training models, as well as the availability of ever-increasing amounts of data. In this chapter, you will learn what deep learning is, the R packages available for training such models, how to get your system set up for analysis, and how to connect R with H2O, which we will use for many of the examples and work in later chapters on how to actually train and use a deep learning model.
In this chapter, we will explore the following topics:
What is deep learning?
R packages that train deep learning models such as deep belief networks or deep neural networks
Connecting R and H2O, the main package we will be using for deep learning