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Fully connected layers are typically used at the end of a CNN to perform the final classification or prediction. These layers take the output of the previous layers (which have been processed by convolutional and pooling layers) and flatten it into a one-dimensional vector. This vector is then fed into a fully connected neural network, where each neuron is connected to every neuron in the previous layer. The fully connected layers learn to combine the features extracted by the convolutional layers to make a final decision about what the image is. For example, in an image classification task, the fully connected layers might learn to recognize combinations of edges, shapes, and textures that are indicative of different objects.
