University⏱ 5 min
Gradient Descent
Optimization on loss landscapes
○Not Started
Gradient Descent on Loss Landscapes
Loss Function
L(x,y) = x² + 2y²
Learning rate α0.050
Momentum β0.000
Status
Position(, )
Loss
‖∇L‖0.0000
Steps-1
x ← x − α∇L(x)
💡 Experiment
Try: 1) Large learning rate → oscillation/divergence. 2) Small lr → slow convergence. 3) Momentum → faster valley traversal. 4) Multi-modal → local minima. Click the map to set a new start.