What are the different types of optimizers used in machine learning?

What are the different types of optimizers used in machine learning?

In machine learning, common optimizers include Gradient Descent, Adam, RMSprop, and Adagrad. Each has its strengths.

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps track of both the average of the gradients and the squared gradients.

What are the advantages of using RMSprop over traditional gradient descent?

RMSprop adjusts the learning rate for each parameter, which helps in dealing with non-stationary objectives and improves convergence.

Are there any specific scenarios where one optimizer is preferred over others?

Yes, for instance, Adam is often preferred for deep learning tasks, while SGD with momentum is favored for training large-scale models.