What is gradient descent and how does it work?

What is gradient descent and how does it work?

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent.

Can you explain how the learning rate affects gradient descent?

The learning rate determines the size of the steps taken towards the minimum. A small learning rate may take longer to converge, while a large one can overshoot.

What are some common variations of gradient descent?

Common variations include Stochastic Gradient Descent (SGD), Mini-batch Gradient Descent, and Momentum-based methods.

How do I choose the right learning rate for my model?

You can experiment with different learning rates, use techniques like learning rate schedules, or employ adaptive learning rate methods.