Deep learning describes a class of optimization methods of artificial neural networks, which have numerous hidden layers between the input layer and the output layer and thus have an extensive internal structure. In extensions of the learning algorithms for network structures with very few or no interlayers, as with the single-layer perceptron, the methods of deep learning enable a stable learning success even with numerous interlayers.