What are the advantages of Adam over SGD?

What are the advantages of Adam over SGD?

Adam optimizer combines the benefits of two other extensions of stochastic gradient descent. It adapts the learning rate for each parameter.

Can you explain how Adam adapts the learning rates?

Sure! Adam maintains two moving averages for each parameter: the first moment (mean) and the second moment (uncentered variance). This helps in adjusting the learning rates dynamically.

Does Adam perform better in all scenarios compared to SGD?

Not necessarily. While Adam often performs better in practice, SGD can outperform Adam in certain cases, especially with well-tuned hyperparameters.

What are some scenarios where SGD might be preferred?

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