How many kinds of NNs exist?
There are many many kinds of NNs by now. Nobody knows exactly how many. New ones (or at least variations of existing ones) are invented every week. Below is a collection of some of the most well known methods, not claiming to be complete. The main categorization of these methods is the distinction between supervised and unsupervised learning: • In supervised learning, there is a “teacher” who in the learning phase “tells” the net how well it performs (“reinforcement learning”) or what the correct behavior would have been (“fully supervised learning”). • In unsupervised learning the net is autonomous: it just looks at the data it is presented with, finds out about some of the properties of the data set and learns to reflect these properties in its output. What exactly these properties are, that the network can learn to recognise, depends on the particular network model and learning method. Usually, the net learns some compressed representation of the data. Many of these learning methods
