What are the differences between SGD and Adam optimizers?

What are the differences between SGD and Adam optimizers?

SGD, or Stochastic Gradient Descent, updates weights using the gradient of the loss function. Adam, on the other hand, adapts the learning rate for each parameter based on first and second moments of the gradients.

Which one is better for training deep learning models?

It depends on the specific problem. Adam often performs better in practice for deep learning due to its adaptive learning rates, but SGD can be more effective for certain tasks with proper tuning.

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages of two other extensions of SGD: AdaGrad and RMSProp. It maintains a moving average of both the gradients and the squared gradients, which helps in adjusting the learning rate dynamically.

Are there any scenarios where SGD is preferred over Adam?

Yes, SGD can be preferred in scenarios where you need more control over the learning process, such as when fine-tuning models or when working with smaller datasets.