Translations:Convolutional Neural Networks/1/en - Revision history

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	'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and pooling, making them far more efficient than fully connected networks for visual and spatial tasks.		'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and {{Term\|pooling}}, making them far more efficient than fully connected networks for visual and spatial tasks.

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	'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and ~~{{Term\|~~pooling}}, making them far more efficient than fully connected networks for visual and spatial tasks.		'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and pooling, making them far more efficient than fully connected networks for visual and spatial tasks.

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	'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and pooling, making them far more efficient than fully connected networks for visual and spatial tasks.		'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks\|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and {{Term\|pooling}}, making them far more efficient than fully connected networks for visual and spatial tasks.

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'''Convolutional neural networks''' ('''CNNs''' or '''ConvNets''') are a class of deep [[Neural Networks|neural networks]] specifically designed to process data with a grid-like topology, such as images (2D grids of pixels), audio spectrograms, and video. They exploit the spatial structure of the input through local connectivity, weight sharing, and pooling, making them far more efficient than fully connected networks for visual and spatial tasks.