Shifting gears away from the Space Shuttle, let's work through a practical example of deep learning, using the h2o
package. We will do this on the data that we used for some of the chapters: the Pima Indian diabetes data. In Chapter 5, More Classification Techniques — K-Nearest Neighbors and Support Vector Machines, the best classifier was the sigmoid kernel, Support Vector Machine. We've already gone through the business and data understanding work in that chapter, so in this section, we will focus on how to load the data in the H20 platform and run the deep learning code.
H2O is an open source predictive analytics platform with prebuilt algorithms, such as k-nearest neighbor, gradient boosted machines, and deep learning. You can upload data to the platform via Hadoop, AWS, Spark, SQL, noSQL, or your hard drive. The great thing about it is that you can utilize the machine learning algorithms in R and, at a much greater scale, on your local machine...