What are advanced gradient descent techniques?

What are advanced gradient descent techniques?

Advanced gradient descent techniques include methods like Adam, RMSprop, and AdaGrad, which adapt the learning rate.

How does the Adam optimizer work?

Adam combines the benefits of two other extensions of stochastic gradient descent: AdaGrad and RMSprop. It maintains a moving average of both the gradients and the squared gradients.

What are the advantages of using these techniques?

These techniques can lead to faster convergence, better performance on complex datasets, and reduced sensitivity to hyperparameters.

Can you explain the difference between Adam and RMSprop?

Sure! Adam uses both the first and second moments of the gradients, while RMSprop only uses the second moment, which can make Adam more robust in some cases.