What are the common challenges faced in gradient descent?

What are the common challenges faced in gradient descent?

Some common challenges include choosing the right learning rate, dealing with local minima, and slow convergence.

How can I choose the right learning rate?

You can start with a small learning rate and gradually increase it, or use techniques like learning rate schedules.

What are local minima and why are they a problem?

Local minima are points where the function value is lower than neighboring points, which can trap the optimization process.

Are there any methods to overcome these challenges?

Yes, techniques like momentum, adaptive learning rates, and using different optimization algorithms can help.

Can you explain momentum in gradient descent?

Momentum helps accelerate gradient descent by adding a fraction of the previous update to the current update, smoothing the path.