To close out the chapter, we will discuss a more realistic use case for neural networks. We will use a public dataset by Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. (2013) that uses smartphones to track physical activity. The data can be downloaded here: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones. The smartphones had an accelerometer and gyroscope from which 561 features from both time and frequency were used.
The smartphones were worn during walking, walking upstairs, walking downstairs, standing, sitting, and lying down. Although this data came from phones, similar measures could be derived from other devices designed to track activity such as various fitness tracking watches or bands. So this data can be useful if we want to sell devices and have them automatically track how many of these different activities the wearer engages in.
This data has been normalized to range from -1 to...