What is the difference between SGD and Adam?

Can you explain the difference between SGD and Adam optimization algorithms?

Sure! SGD, or Stochastic Gradient Descent, updates parameters using the average of the gradients from a mini-batch of data. Adam, on the other hand, combines the benefits of two other extensions of SGD: AdaGrad and RMSProp, adapting the learning rate for each parameter.

What are the advantages of using Adam over SGD?

Adam generally converges faster than SGD because it adjusts the learning rate based on the first and second moments of the gradients. This can lead to better performance on complex problems.

Are there any situations where SGD might be preferred over Adam?

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

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

Certainly! Use Adam for training deep learning models with large datasets where speed is crucial. Use SGD for simpler models or when you want to ensure a more stable convergence.