What are some effective optimization techniques in deep learning?

What are some effective optimization techniques in deep learning?

Some effective optimization techniques include Stochastic Gradient Descent (SGD), Adam, and RMSprop.

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages 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 robust to noisy data and sparse gradients.

Are there any specific scenarios where SGD is preferred?

Yes, SGD can be preferred in scenarios with large datasets and when fine-tuning models, as it can lead to better generalization.