Momentum

Momentum is a fundamental concept in optimization techniques, particularly in machine learning and deep learning. It refers to a method that enhances the efficiency of gradient descent algorithms by incorporating the past gradients to accelerate convergence. By maintaining a velocity vector that accumulates past gradients, momentum helps to navigate through the loss landscape more effectively, reducing oscillations and improving stability. This technique is especially beneficial in scenarios with noisy data, allowing models to learn more robustly. Overall, momentum serves as a powerful tool for optimizing neural networks and improving their performance in various applications.