How is calculus used in machine learning?

How is calculus used in machine learning?

Calculus is fundamental in machine learning for optimization, particularly in training models through gradient descent.

Can you explain gradient descent?

Sure! Gradient descent is an optimization algorithm that minimizes the loss function by iteratively moving in the direction of the steepest descent.

What role does the derivative play in this process?

The derivative indicates the slope of the loss function, helping to determine the direction and rate of change for optimization.

Are there other optimization methods besides gradient descent?

Yes, other methods include stochastic gradient descent, Adam, and Newton's method, each with its own advantages.