Momentum: A Key Component in Modern Optimization Algorithms

Momentum Technique in Deep Learning Momentum is a technique used to accelerate the convergence of gradient descent algorithms in deep learning. By incorporating a moving average of past gradients, momentum helps to smooth out the optimization process and prevent oscillations, leading to faster convergence. How Momentum Works In standard gradient descent, model parameters are updated directly in the direction of the negative gradient of the loss function. However, this approach can be slow and susceptible to getting stuck in local minima, especially in high-dimensional parameter spaces. Momentum addresses these issues by introducing a velocity term that accumulates the gradients of the past steps and uses this accumulated gradient to update the parameters.