What is the effect of feature scaling on gradient descent convergence?

Feature scaling improves convergence speed by normalizing gradient magnitudes.

Without scaling, features with large magnitudes dominate gradients, causing zig-zag optimization paths. Scaling transforms cost contours into near-circular shapes, enabling faster and more stable convergence.