There are basically two types of learning for neural networks, namely supervised, and unsupervised. The learning in the human mind, for example, also works in this way. We are able to build knowledge from observations without any target (unsupervised) or we can have a teacher who shows us the right pattern to follow (supervised). The difference between these two paradigms relies mainly on the relevancy of a target pattern, and varies from problem to problem.
This learning type deals with pairs of xs (independent values), and ys (dependent values) with the objective to map them in a function . Here the Y data is the supervisor, the target desired outputs, and the X are the source independent data that jointly generate the Y data. It is analogous to a teacher who is teaching somebody a certain task to be performed:
One particular feature of this learning paradigm is that there is a direct error reference which is just the comparison between the target and...