In article 13 about optimization, we mentioned optimization methods, significantly specializing in gradient descent. the earlier article have highlighted the potential concern of gradual convergence with gradient descent, particularly in circumstances of comparatively flat gradients. To beat this problem, conjugate gradient descent presents an answer for accelerating convergence. Moreover, momentum gradient descent presents one other efficient method, which we are going to discover additional on this article.
Within the context of optimization algorithms like gradient descent, momentum may be envisioned as akin to a ball rolling down an almost flat incline. Simply as a ball gathers momentum whereas descending because of the power of gravity, momentum refers back to the gathered affect of previous gradients on the present replace path. When the gradient is comparatively flat, as on a mild slope, conventional gradient descent could proceed slowly. Nonetheless, by incorporating momentum, the optimization course of beneficial properties a ‘reminiscence’ of previous gradients, permitting it to keep up and even speed up its tempo, akin to how the rolling ball beneficial properties pace over time. Consequently, momentum gradient descent navigates via flat areas or shallow native minima extra effectively, facilitating quicker convergence towards…
