What are some common issues with gradient descent?

What are some common issues with gradient descent?

Common issues with gradient descent include getting stuck in local minima, slow convergence, and sensitivity to learning rates.

How can I avoid getting stuck in local minima?

You can use techniques like momentum, adaptive learning rates, or try different initialization methods to help avoid local minima.

What is the impact of learning rate on gradient descent?

The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated. A rate too high can cause divergence, while too low can slow convergence.

Are there alternatives to gradient descent?

Yes, alternatives include stochastic gradient descent, mini-batch gradient descent, and optimization algorithms like Adam or RMSprop.