in setting a learning rate, there is a trade-off between the rate of convergence and overshooting. while the direction toward the minimum is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that direction. a too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.