tensorflow tips functional api feedfoward network with multiple inputs and outputs
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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, enabling the network to capture intricate data patterns. Common functions like sigmoid, tanh, and ReLU each offer unique benefits. Training involves backpropagation, where the network adjusts weights
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