What are the different variations of SGD optimizers?

What are the different variations of SGD optimizers?

There are several variations of Stochastic Gradient Descent (SGD) optimizers, including Momentum, Nesterov Accelerated Gradient, Adagrad, RMSprop, and Adam. Each has its own advantages and use cases.

Can you explain how Adam optimizer works?

Sure! The Adam optimizer combines the benefits of two other extensions of SGD: AdaGrad and RMSProp. It computes adaptive learning rates for each parameter from estimates of first and second moments of the gradients.

What are the advantages of using RMSprop over standard SGD?

RMSprop adjusts the learning rate for each parameter individually, which helps to stabilize the updates and can lead to faster convergence, especially in non-stationary settings.

Are there any specific scenarios where I should avoid using SGD?

Yes, SGD can struggle with noisy gradients or when the loss surface has sharp curvatures. In such cases, using optimizers like Adam or RMSprop might yield better results.