What are the different variants of Stochastic Gradient Descent?

What are the different variants of Stochastic Gradient Descent?

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

Can you explain how Adam differs from traditional SGD?

Adam combines the benefits of two other extensions of SGD: AdaGrad and RMSProp. It adapts the learning rate for each parameter based on the first and second moments of the gradients, which helps in faster convergence.

What are the advantages of using Mini-batch SGD?

Mini-batch SGD allows for faster convergence by updating weights more frequently than full-batch methods, while also reducing the variance of the parameter updates.

Are there any disadvantages to using SGD variants?

Yes, some disadvantages include the potential for getting stuck in local minima, sensitivity to hyperparameters, and the need for careful tuning to achieve optimal performance.