Lemmatization

Learning Rate Scheduling is a technique in machine learning that systematically adjusts the learning rate during model training to enhance convergence efficiency and accuracy. By modulating the learning rate—either decreasing or increasing it at specific intervals—it helps the model escape local minima, stabilize training, and achieve better generalization performance. Various scheduling strategies include step decay, exponential decay, and cyclical learning rates.