The learning rate η is a hyperparameter that must be adjusted. If it's too high, hyperparameter the learner will take steps that are too large, overshooting the minimum of the loss function. If it's too low, the learner will take steps that are too small, and take too long to get to the minimum. It is common to start with a higher learning rate and then slowly decrease it, so that it is a function of the iteration k of training; the notation η k can be used to mean the value of the learning rate at iteration k.