When should I use RMSprop in my machine learning models?

When should I use RMSprop in my machine learning models?

RMSprop is ideal for non-stationary objectives, like in recurrent neural networks or when dealing with noisy gradients.

What are the advantages of using RMSprop over other optimizers?

RMSprop adapts the learning rate for each parameter, which helps in faster convergence and better performance in many cases.

Are there specific scenarios where RMSprop performs poorly?

Yes, RMSprop may struggle with very sparse gradients or when the learning rate is not tuned properly.

How does RMSprop compare to Adam optimizer?

Adam combines RMSprop's adaptive learning rate with momentum, often leading to better performance in practice.