ĬNNs are regularized versions of multilayer perceptrons. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks ( SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation- equivariant responses known as feature maps. In deep learning, a convolutional neural network ( CNN, or ConvNet) is a class of artificial neural network ( ANN), most commonly applied to analyze visual imagery.
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