What are some common optimizers used in machine learning?

What are some common optimizers used in machine learning?

Some common optimizers include Stochastic Gradient Descent (SGD), Adam, RMSprop, and Adagrad. Would you like to know more about any specific one?

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps an exponentially decaying average of past gradients (momentum) and also maintains an exponentially decaying average of past squared gradients (RMSprop). This helps in adapting the learning rate for each parameter.

What are the advantages of using Adam over SGD?

Adam generally converges faster than SGD because it adjusts the learning rate for each parameter individually, which can lead to better performance in practice. It also requires less tuning of the learning rate.

Are there any disadvantages to using Adam?

Yes, while Adam is powerful, it can sometimes lead to overfitting, and it may not generalize as well as SGD in some cases. It's important to experiment with different optimizers based on your specific problem.