Fully Connected Layer vs. Convolutional Layer: Explained | Built In
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Fully Connected Layer vs. Convolutional Layer: Explained | Built In
A fully connected layer is a type of neural network layer where each neuron is connected to every neuron in the previous layer, enabling global feature learning. A convolutional layer, by contrast, uses filters that connect only to local regions of the input, allowing it to detect spatial patterns like edges or textures while using fewer ...
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