👉 Momentum computing is an innovative approach to machine learning that enhances the efficiency and performance of neural networks by leveraging the concept of "momentum" from physics. In traditional gradient descent methods, updates to model parameters are made in the direction of the steepest decrease in the loss function. Momentum computing introduces a velocity term that accumulates past gradients, effectively smoothing out oscillations and accelerating convergence in the relevant direction. This is achieved by adding a fraction of the previous update to the current one, creating a "momentum" that helps the model navigate through flat regions and avoid local minima. By doing so, momentum computing can lead to faster training times and improved model performance, particularly in deep learning tasks where the loss landscape can be highly complex.
