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

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

In deep learning, common optimizers include SGD, Adam, RMSprop, and Adagrad. Each has its own advantages.

Can you explain how Adam optimizer works?

Sure! Adam combines the benefits of two other extensions of SGD: AdaGrad and RMSProp. It computes adaptive learning rates for each parameter.

What are the advantages of using Adam over SGD?

Adam generally converges faster than SGD and is more efficient with sparse gradients, making it suitable for large datasets.

Are there any situations where SGD is preferred over Adam?

Yes, SGD can be preferred in scenarios where fine-tuning is needed, as it can lead to better generalization in some cases.