What are the differences between Adam optimizer and SGD?

What are the differences between Adam optimizer and SGD?

Adam optimizer and SGD (Stochastic Gradient Descent) are both optimization algorithms used in training machine learning models. Adam adapts the learning rate for each parameter, while SGD uses a fixed learning rate. Adam generally converges faster and is more efficient for large datasets.

When should I use Adam over SGD?

You should consider using Adam when you have a large dataset or when your model has many parameters. It's particularly useful for problems with noisy gradients or when you want faster convergence.

Are there any downsides to using Adam?

Yes, while Adam is powerful, it can sometimes lead to overfitting, especially on smaller datasets. Additionally, it may not generalize as well as SGD in some cases.

Can I combine both optimizers?

Yes, some practitioners use a combination of both optimizers, starting with SGD for a few epochs to get a good baseline and then switching to Adam for fine-tuning.