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Deep learning is set of algorithms in machine learning that attempt to learn layered models of inputs, commonly neural networks. The layers in such models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts.
Deep learning is part of a broader family of machine learning methods based on learning representations. An observation (e.g., an image) can be represented in many ways (e.g., a vector of pixels), but some representations make it easier to learn tasks of interestfrom examples, and research in this area attempts to define what makes better representations and how to learn them. The term "deep learning" gained traction in the mid-2000s after a publication by Geoffrey Hinton[2][3] showed how a many-layered neural network could be effectively trained one layer at a time, treating each layer in turns as a restricted Boltzmann machine. Although the backpropagation algorithm had been available for training neural networks since 1986, it was often considered too slow for practical use.[2] As a result, neural networks fell out of favor in practical machine learning and simpler models such as support vector machines dominated much of the field in the 1990s.