A convolutional layer is a fundamental building block of Convolutional Neural Networks (CNNs) that applies convolution operations to input data, typically images, to extract features. This layer uses filters or kernels that slide over the input data to create feature maps, capturing spatial hierarchies and patterns in the data while reducing dimensionality. The convolutional layer plays a crucial role in the effectiveness of CNNs for tasks like image recognition and classification.
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