Hands-On Python Deep Learning for the Web

Unsupervised learning

Unsupervised learning presents itself in scenarios where the training samples do not carry with them output feature(s). You could wonder then, what are we supposed to learn or predict in such situations? The answer is similarity. In more elaborate terms, when we have a dataset for unsupervised learning, we're usually trying to learn the similarity between the training samples and then to assign classes or labels to them.

Consider a crowd of people standing in a large field. All of them have features such as age, gender, marital status, salary range, and education level. Now, we wish to group them based on their similarities. We decide to form three groups and see that they arrange themselves in a manner of gender—a group ...