In the rugged landscape of Deep Learning loss functions, standard Gradient Descent often struggles with local minima, saddle points, and the infamous "zig-zag" path. This article breaks down the geometry of loss landscapes—from 2D curves to 3D contours—and explains how Momentum Optimization acts as a confident driver. Learn how using a simple velocity term and the "moving average" of past gradients can significantly accelerate model convergence and smooth out noisy training p