Convolutional Layers
Convolutional Layers are a type of neural network layer that are commonly used in image and video recognition tasks. They consist of a set of learnable filters that are convolved with the input data to produce a set of output features. Each filter slides over the input data, performing a dot product between the filter weights and the input values at each position. The resulting output is a feature map that highlights the presence of certain features in the input data. Convolutional Layers are designed to capture local spatial dependencies in the input data, allowing the network to learn hierarchical representations of the input. They are often followed by pooling layers, which reduce the spatial dimensions of the output features, and activation functions, which introduce non-linearity into the network.
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