How do different machine learning optimizers compare?

What are the main differences between Adam and SGD optimizers?

Adam is adaptive and adjusts learning rates for each parameter, while SGD uses a fixed learning rate.

Which one is generally better for deep learning tasks?

Adam often performs better in deep learning due to its adaptive nature, especially with large datasets.

Are there situations where SGD might outperform Adam?

Yes, SGD can outperform Adam in scenarios with smaller datasets or when fine-tuning models.

What about other optimizers like RMSprop?

RMSprop is similar to Adam but focuses on maintaining a moving average of the squared gradients.

How do I choose the right optimizer for my model?

Consider the dataset size, model architecture, and specific task requirements when choosing an optimizer.