Learning Rate Schedule

A learning rate schedule is a strategy employed in the training of machine learning models to adjust the learning rate dynamically over time. The learning rate, a hyperparameter that controls the step size during optimization, can significantly influence convergence behavior. Common strategies include exponential decay, step decay, and cyclical learning rates. Mathematically, a learning rate schedule can be expressed as a function of the epoch number or iteration count, allowing for a gradual decrease in learning rate to facilitate convergence to a minimum. This approach helps in avoiding overshooting the minimum and can lead to improved training stability and performance. The learning rate schedule is closely related to optimization algorithms such as Stochastic Gradient Descent (SGD) and its variants, where the choice of learning rate directly impacts the optimization trajectory.