Convolutional Neural Networks are mostly used in computer vision. In contrast to fully linked layers in MLPs, one or more convolution layers extract simple characteristics from input by performing convolution operations in CNN models. Each layer is made up of nonlinear functions of weighted sums at various coordinates of spatially close subsets of the previous layer's outputs, allowing the weights to be reused.
The AI system learns to automatically extract the properties of these inputs to fulfill a specific task, such as picture classification, face identification, and image semantic segmentation, given a sequence of images or videos from the actual world.
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