convolutional layers
Convolutional layers are a fundamental component of convolutional neural networks (CNNs) that perform feature extraction by applying a set of filters to input data, such as images, in a systematic and localized manner. These layers convolve the filters over the input, capturing and emphasizing different patterns and spatial structures, ultimately enabling the network to learn hierarchical representations of the data.
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Similar Concepts
- attention layers
- autoencoder layers
- batch normalization layers
- convolutional neural networks
- convolutional neural networks (cnn)
- dropout layers
- embedding layers
- fully connected layers
- generative adversarial network (gan) layers
- long short-term memory (lstm) layers
- multilayer perceptrons
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