How is calculus used in deep learning?

How is calculus used in deep learning?

Calculus is fundamental in deep learning for optimizing neural networks, particularly through gradient descent.

Can you explain gradient descent?

Gradient descent is an optimization algorithm that minimizes the loss function by adjusting weights in the direction of the steepest descent.

What role do derivatives play in this process?

Derivatives help determine the slope of the loss function, guiding how much to change the weights to reduce error.

Are there other optimization methods besides gradient descent?

Yes, other methods include stochastic gradient descent, Adam, and RMSprop, each with unique advantages.