Unsupervised learning

At the other extreme, we have the so-called unsupervised learning, which assumes no supervision and has no known labels assigned to our data. The main objective is to learn some hidden structure of the dataset at hand. One common example of such an approach to learning is the clustering of data. This happens when our algorithm tries to combine data items into a set of clusters, which can reveal relationships in data. For instance, you might want to find similar images or clients with common behaviors.

Another unsupervised learning method that is becoming more and more popular is generative adversarial networks (GANs). When we have two competing neural networks, the first network is trying to generate fake data to fool the second network, while the second network is trying to discriminate artificially generated data from data sampled from our dataset. Over time, both networks become more and more skillful in their tasks by capturing subtle specific patterns...