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A feed forward network comprises distinct layers - input, hidden, and output - with neurons meticulously interconnected between layers. This architecture facilitates the gradual transformation of raw input into meaningful predictions through forward propagation. During this process, data flows layer by layer, undergoing operations determined by weighted connections and bias terms. Activation functions introduce vital non-linearity.