📉 Gradient Descent Optimizer

Visualize how gradient descent finds the minimum of a function

Function:

Learning Rate: 0.1

Momentum: 0.0

Optimization Path

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Iterations

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Current Loss

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X Position

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Y Position

📚 How to Use

Select Function: Choose different optimization landscapes
Learning Rate: Controls step size (too high = overshooting, too low = slow)
Momentum: Helps escape local minima and accelerate convergence
Start: Watch gradient descent find the minimum!
Add Random Start: Try multiple starting points simultaneously
Observe: See how parameters affect convergence speed and stability