Explore the complex optimization landscape of machine learning algorithms in an immersive 3D environment. Watch as gradient descent navigates through intricate mathematical terrains to find optimal solutions.
Initializing 3D Visualization...
Gradient descent is an optimization algorithm used to minimize functions by iteratively moving in the direction of steepest descent as defined by the negative of the gradient.
The algorithm calculates the gradient of the loss function and updates parameters in the opposite direction, scaled by the learning rate.
This visualization shows a complex 3D loss surface where gradient descent navigates through valleys and peaks to find the global minimum.