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These extracted features are then passed through activation functions. The activation function adds non-linearity to the network, which is essential for learning complex patterns. Think of it like this: without non-linearity, the network can only learn linear relationships. Activation functions allow the network to model more intricate and realistic data. Finally, the information from these layers flows into the fully connected layers, where the network makes its final classification.
