What are the advantages of different variants of gradient descent?

What are the advantages of different variants of gradient descent?

Gradient descent has several variants, each with unique advantages. For instance, Stochastic Gradient Descent (SGD) can converge faster on large datasets.

What about Adam optimizer? How does it compare?

The Adam optimizer combines the benefits of AdaGrad and RMSProp, adapting the learning rate for each parameter, which often leads to faster convergence.

Are there any disadvantages to using these variants?

Yes, while variants like Adam are powerful, they can sometimes lead to overfitting or may not generalize well on certain tasks.

Which variant would you recommend for deep learning?

For deep learning, Adam is often recommended due to its adaptive learning rates, but it's essential to experiment with different optimizers based on your specific problem.

Can you explain how momentum works in gradient descent?

Momentum helps accelerate gradient descent by adding a fraction of the previous update to the current update, smoothing out the optimization path.