What are the advantages of using gradient descent in machine learning?

What are the advantages of using gradient descent in machine learning?

Gradient descent is a powerful optimization algorithm used to minimize the loss function in machine learning models. Its advantages include efficiency in handling large datasets, the ability to escape local minima, and adaptability to various types of models.

Can you explain how it handles large datasets?

Certainly! Gradient descent processes data in batches, which allows it to update model parameters incrementally. This means it can work with large datasets without needing to load everything into memory at once.

What about its ability to escape local minima?

Gradient descent can escape local minima by using techniques like momentum or adaptive learning rates, which help it to continue moving in the direction of the global minimum even if it gets stuck temporarily.

Are there any disadvantages to using gradient descent?

Yes, some disadvantages include sensitivity to the choice of learning rate, potential for slow convergence, and the risk of getting stuck in local minima without advanced techniques.