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How does backpropagation happen in a feed-forward neural network when there is nothing connecting the output to input?
How does the learning rate affect backpropagation?
The learning rate is an important hyperparameter that determines how much the weights are adjusted during each iteration of the algorithm. If the learning rate is too large, the weights may oscillate and fail to converge to a solution. If the learning rate is too small, the algorithm may take a long time to converge to a solution. The optimal learning rate depends on the problem being solved and the size of the network.