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How does backpropagation happen in a feed-forward neural network when there is nothing connecting the output to input?
But how does the algorithm know which weights to adjust if there are no connections between the output layer and input layer?
Although the output layer may not have direct connections to the input layer, the error calculated during backpropagation can still be propagated backwards through the layers. During each iteration of the algorithm, the weights are adjusted in a way that minimizes the error between the predicted output and actual output. The gradients are calculated by taking the partial derivative of the loss function with respect to the weights.