in order to achieve faster convergence, prevent oscillations and getting stuck in undesirable local minima the learning rate is often varied during training either in accordance to a learning rate schedule or by using an adaptive learning rate. in newton's method, the learning rate is essentially determined from the local curvature of the loss function, by using the inverse of the hessian matrix as the step size.