How do RMSprop and Adam compare in optimizing neural networks?

Can you explain the differences between RMSprop and Adam optimizers?

Sure! RMSprop adjusts the learning rate for each parameter based on the average of recent gradients, while Adam combines the benefits of RMSprop with momentum by maintaining a moving average of both the gradients and their squared values.

Which one is generally better for training deep learning models?

It depends on the specific problem and dataset. Adam is often preferred for its adaptive learning rates, but RMSprop can perform better in some cases, especially with non-stationary objectives.

Are there any scenarios where RMSprop outperforms Adam?

Yes, RMSprop can be more effective in scenarios with noisy gradients or when training on smaller datasets, as it can adapt more quickly to changes.

What are the hyperparameters for both optimizers?

RMSprop typically has a learning rate and decay rate, while Adam has a learning rate, beta1, and beta2 for the moving averages.