What are some common issues with Stochastic Gradient Descent?

What are some common issues with Stochastic Gradient Descent?

Some common issues with Stochastic Gradient Descent (SGD) include slow convergence, getting stuck in local minima, and sensitivity to learning rates.

How can I address slow convergence in SGD?

To address slow convergence, you can try using a learning rate schedule, momentum, or adaptive learning rate methods like Adam.

What does it mean to get stuck in local minima?

Getting stuck in local minima means that the algorithm has found a solution that is better than its neighbors but not the best overall solution.

Are there any techniques to avoid local minima?

Yes, techniques like using a larger learning rate, adding noise to the gradients, or employing techniques like simulated annealing can help.

What is the impact of learning rate on SGD?

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