Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are a type of deep neural network that are commonly used for image and video recognition tasks. They are designed to automatically and adaptively learn spatial hierarchies of features from input data. CNNs consist of multiple layers of small neuron collections which process portions of the input image, called receptive fields. The outputs of these collections are then tiled so that they overlap, to obtain a better representation of the original image; this is called feature maps. CNNs are trained using backpropagation, where the weights of the network are adjusted to minimize the difference between the predicted and actual outputs. They have been shown to achieve state-of-the-art results on a variety of computer vision tasks, including image classification, object detection, and segmentation.
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