What are the main challenges in gradient descent?

What are the main challenges in gradient descent?

The main challenges in gradient descent include local minima, saddle points, and choosing the right learning rate.

Can you explain what local minima are?

Local minima are points where the function value is lower than at neighboring points, but not necessarily the lowest overall.

How does the learning rate affect the optimization process?

A learning rate that's too high can cause overshooting, while one that's too low can slow down convergence.

What are saddle points and why are they problematic?

Saddle points are flat regions where the gradient is zero, making it hard for the algorithm to determine the direction to move.

Are there any techniques to overcome these challenges?

Yes, techniques like momentum, adaptive learning rates, and using advanced optimizers like Adam can help.