I was doing some research on how backpropagation works? I read that, backpropagation is used to find the optimal weight of each neuron after every iteration using partial derivates and updates the weights of the neuron.
On the other hand, we have hyperparameter called 'learning-rate' used to update the weight of the neuron in each iteration by calculating the direction of the error.
These two cases look like working independently, I mean, while backpropagation algorithm itself finding the optimal weight, we do not need a learning rate parameter itself.
Is my understanding correct? Please correct me if I am wrong.