How do different gradient descent optimizers compare?

How do different gradient descent optimizers compare?

There are several gradient descent optimizers like SGD, Adam, and RMSprop. Each has its strengths and weaknesses.

What are the main differences between SGD and Adam?

SGD updates parameters using the average of gradients, while Adam adapts the learning rate based on first and second moments.

When should I use RMSprop over Adam?

RMSprop is often preferred for non-stationary objectives, while Adam is generally more robust across various tasks.

Can you give me an example of when to use each optimizer?

Sure! Use SGD for simple tasks with a lot of data, Adam for complex models, and RMSprop for recurrent neural networks.