We've been acquainted with this type of learning in Chapter 2, How Neural Networks Learn, and now, we are going to explore the features of this learning paradigm in a detailed fashion. Unsupervised learning algorithms in essence aim at finding patterns within datasets by using only the information presented in the datasets themselves. Here, the unsupervised learning algorithm will adjust the parameters (weights in the case of neural networks) without any error measure, and this is the crucial feature that distinguishes unsupervised from supervised learning. The learning itself is triggered only on the basis of the fact that in neurology, similar stimuli produce similar responses. So, applying this fundamental knowledge to artificial neural networks, we can say that similar data produce similar outputs, and these outputs can be grouped in clusters.
Although this learning may be used in other mathematical fields such as statistics, its core functionality...