This chapter discusses deep learning, a powerful multilayered 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, and how to get your system set up for analysis. We will briefly discuss MXNet and Keras, which are the two main frameworks that we will use for many of the examples in later chapters to actually train and use deep learning models.
In this chapter, we will explore the following topics:
- What is deep learning?
- A conceptual overview of deep learning
- Setting up your R environment and the deep learning frameworks available in R
- GPUs and reproducibility
