In this chapter, we used a generative adversarial network to illustrate how to generate images of a single handwritten digit. Generative adversarial networks make use of two networks: generator and discriminator networks. Generator networks create fake images from data containing random noise, while discriminator networks are trained to differentiate between fake images and real images. These two networks compete against each other so that realistic-looking fake images can be created. Although in this chapter we provided an example of using a generative adversarial network to generate new images, these networks are also known to have applications in generating new text or new music, as well as in anomaly detection.

In this section, we went over various deep learning networks that are useful for dealing with image data. In the next section, we will go over deep learning networks for natural language processing.