Deep Learning with TensorFlow

Unsupervised learning

In unsupervised learning, a set of inputs is supplied to the system during the training phase which, however, contrary to the case supervised learning, is not labeled with the related belonging class. This type of learning is important because in the human brain it is probably far more common than supervised learning.

The only objects in the domain of learning models, in this case, are the observed data inputs, which often is assumed to be independent samples of an unknown underlying probability distribution.

Unsupervised learning algorithms are used particularly used in clustering problems, in which given a collection of objects, we want to be able to understand and show their relationships. A standard approach is to ...