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How does backpropagation happen in a feed-forward neural network when there is nothing connecting the output to input?
Can you explain how backpropagation happens in a feed-forward neural network when there is nothing connecting the output to input?
Sure! In a feed-forward neural network, the output of one layer serves as the input for the next layer. During the forward pass, the input is multiplied by a set of weights and biases until it reaches the output layer. During the backpropagation process, the error between the predicted output and actual output is calculated and the weights are adjusted backwards from the output layer to the input layer.