When should I use Stochastic Gradient Descent?

When should I use Stochastic Gradient Descent?

Stochastic Gradient Descent (SGD) is ideal for large datasets and online learning. It updates weights more frequently.

What are the advantages of using SGD over other optimization methods?

SGD can converge faster than batch methods, especially with large datasets, and it helps escape local minima.

Are there any downsides to using SGD?

Yes, SGD can be noisy and may require careful tuning of the learning rate to ensure convergence.

How do I choose the right learning rate for SGD?

You can start with a small learning rate and use techniques like learning rate schedules or adaptive methods to adjust it.