How do SGD and Adam compare in terms of performance?

How do SGD and Adam compare in terms of performance?

SGD (Stochastic Gradient Descent) is a simple and effective optimization algorithm, but it can be slow to converge. Adam, on the other hand, adapts the learning rate for each parameter, often leading to faster convergence and better performance in practice.

What are the advantages of using Adam over SGD?

Adam's main advantages include adaptive learning rates, which help it perform well on problems with sparse gradients, and it often requires less tuning of hyperparameters compared to SGD.

Are there any scenarios where SGD performs better than Adam?

Yes, in some cases, especially with large datasets and simple models, SGD can outperform Adam due to its simplicity and lower memory requirements.

Can you give examples of tasks where Adam is preferred?

Adam is often preferred in tasks like training deep neural networks, especially in natural language processing and computer vision, where it handles complex loss landscapes well.