Artificial neural networks are inspired by the structure of biological neural networks and are capable of machine learning, as well as pattern recognition. They are commonly used for both regression and classification problems, comprising a wide variety of algorithms and variations for all manner of problem types. Some popular classification methods are perceptron, restricted Boltzmann machine (RBM), and deep belief networks.