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深度学习通常使用'卷积神经网络 ConvolutionalNeural Networks CNNs''' ，其起源可以追溯到1980年由福岛邦彦引进的新认知机。<ref name="FUKU1980">{{cite journal | last1 = Fukushima | first1 = K. | year = 1980 | title = Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | url = | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | doi=10.1007/bf00344251 | pmid=7370364}}</ref> 1989年扬·勒丘恩(Yann LeCun)和他的同事将反向传播算法应用于这样的架构。在21世纪初，在一项工业应用中，CNNs已经处理了美国大约10% 到20%的签发支票。<ref name="lecun2016slides">Yann LeCun (2016). Slides on Deep Learning [https://indico.cern.ch/event/510372/ Online]

深度学习通常使用'''卷积神经网络 ConvolutionalNeural Networks CNNs''' ，其起源可以追溯到1980年由福岛邦彦引进的新认知机。<ref name="FUKU1980">{{cite journal | last1 = Fukushima | first1 = K. | year = 1980 | title = Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | url = | journal = Biological Cybernetics | volume = 36 | issue = 4| pages = 193–202 | doi=10.1007/bf00344251 | pmid=7370364}}</ref> 1989年扬·勒丘恩(Yann LeCun)和他的同事将反向传播算法应用于这样的架构。在21世纪初，在一项工业应用中，CNNs已经处理了美国大约10% 到20%的签发支票。<ref name="lecun2016slides">Yann LeCun (2016). Slides on Deep Learning [https://indico.cern.ch/event/510372/ Online]</ref>