What is a Convolutional Layer?
Convolutional layers are the building blocks of convolutional neural networks (CNNs), which are primarily used for tasks that require the recognition and processing of spatial data, such as images and videos. These layers apply a convolution operation to the input, passing the result to the next layer.
Fully Connected Layer vs Convolutional Layer
Confusion between Fully Connected Layers (FC) and Convolutional Layers is common due to terminology overlap. In CNNs, convolutional layers are used for feature extraction followed by FC layers for classification that makes it difficult for beginners to distinguish there roles.
This article compares Fully Connected Layers (FC) and Convolutional Layers (Conv) in neural networks, detailing their structures, functionalities, key features, and usage in deep learning architectures.
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