This chapter presented a brief introduction to NNs and deep neural networks. Using multiple hidden layers, deep neural networks have been a revolution in machine learning by providing a powerful unsupervised learning and feature extraction component that can be standalone or integrated as part of a supervised model.

There are many applications of such models, and they are being increasingly used by large companies such as Google, Microsoft, and Facebook. Examples of tasks for deep learning are image recognition (for example, automatically tagging faces, or identifying keywords for an image), voice recognition, and text translation (for example, to go from English to Spanish, or vice versa). Work is even being done on text recognition such as sentiment analysis to try to identify whether a sentence or paragraph is generally positive or negative, particularly useful for evaluating perceptions about a product or service. Imagine being able to scrape reviews and social media for any mention of your product and being able to analyse whether it was being discussed more or less favourably than the month or year before!

This chapter also showed how to set up R and the necessary software and packages installed, in a reproducible way to match the versions used in this book.

In the next chapter, we will begin to train neural networks and generate our own predictions.