Visualizing the Loss Landscape

What is Gradient Descent Actually Doing?

Imagine you are blindfolded on a hilly landscape. Your goal is to reach the lowest point in the valley (minimum error).

1. You feel the slope under your feet (calculate gradient).
2. You take a step down the steepest slope (update weights).
3. Repeat until the slope is flat (convergence).

Learning Rate Impact

• Too Small: Takes thousands of tiny steps (slow training).
• Too Large: Steps right over the valley, overshooting and diverging (exploding gradients).
• Just Right: Smoothly slides to the bottom.