What are some common issues with gradient descent?

What are some common issues with gradient descent?

Some common issues with gradient descent include slow convergence, getting stuck in local minima, and oscillations.

How can I fix slow convergence?

You can try using a larger learning rate, or implement adaptive learning rate methods like Adam or RMSprop.

What do you mean by local minima?

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

How can I avoid getting stuck in local minima?

You can use techniques like momentum, simulated annealing, or try different initializations to escape local minima.