Feed-forward networks are a type of artificial neural network where connections between the nodes do not form cycles. In these networks, data moves in one direction only—from input nodes, through hidden layers, to output nodes. This architecture is fundamental in computational tasks like secondary structure prediction, as it allows for efficient processing of sequential data without the complications introduced by feedback loops.
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