How to Play Gradient Descent

Gradient Descent is a physics-puzzle disguised as a machine-learning optimizer. Launch a loss particle across a noisy loss landscape where local minima (gravity wells) bend its trajectory, and try to settle it into the global minimum (the goal).

Drag back from your loss particle to set the gradient direction
The longer you drag, the larger the learning rate (more power)
Release to take an optimization step toward the global minimum
Local minima exert gravitational pull that warps your path

Tuning Your Hyperparameters

Convergence requires understanding how each local minimum distorts your trajectory. Heavy wells have stronger pull — use them to slingshot around regularization barriers or curve into the global minimum. Each run has a target epoch count (par), but the real challenge is converging in as few steps as possible. The gradients can be friend or foe depending on your approach angle and learning rate.

Slop Fact: Real optimizers use momentum to slingshot past local minima and saddle points — the same trick that lets you ace these descent runs.