What are iterative methods in machine learning?

What are iterative methods in machine learning?

Iterative methods are algorithms that refine solutions through repeated cycles, improving accuracy with each iteration.

Can you give me an example of an iterative method?

Sure! Gradient descent is a popular iterative method used to minimize the loss function in machine learning.

How does gradient descent work?

Gradient descent updates model parameters by moving in the direction of the steepest descent of the loss function.

What are the advantages of using iterative methods?

They can handle large datasets and complex models efficiently, often converging to a solution faster than direct methods.