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 traditional gradient descent?

SGD converges faster and can escape local minima due to its noisy updates, making it suitable for complex models.

Are there any specific scenarios where SGD is not recommended?

Yes, SGD may not be ideal for small datasets or when high precision is required, as it can introduce more variance.

How do I choose the learning rate for SGD?

Choosing the learning rate can be tricky. Start with a small value and use techniques like learning rate schedules or adaptive methods.