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Deep learning can include machine learning algorithms which can use multiple layers of non-linear processing units or elements. Each layer can use the output from a higher layer as input. Deep learning systems can work in a supervised setting, e.g. using one more classification systems. Deep learning systems can also work in an unsupervised setting, e.g. in the context of texture analysis or pattern recognition. Deep learning systems can learn multiple levels of representations that correspond to different levels of abstraction. The different levels can form an order or a hierarchy of concepts. The different layers of a deep learning system can reside in different layers of an artificial neural network, i.e. a deep neural network. They can include hidden layers in an ANN. Deep learning systems and deep ANNs can utilize Boltzmann machines. With deep learning systems, layers can correspond to layers of abstraction, e.g. across a deep neural network. Varying numbers of layers and layer sizes can provide different degrees of abstraction. Higher level, more complex concepts can be learned from lower level layers.

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