When should I use the Adam optimizer in machine learning?

When should I use the Adam optimizer in machine learning?

The Adam optimizer is great for problems with large datasets and high-dimensional spaces. It adapts the learning rate for each parameter.

What are the advantages of using Adam over other optimizers?

Adam combines the benefits of two other extensions of stochastic gradient descent: AdaGrad and RMSProp. It handles sparse gradients well and is computationally efficient.

Are there any situations where I should avoid using Adam?

Yes, in some cases, like when training very small datasets or when you need a very stable convergence, simpler optimizers like SGD might be better.

How do I implement Adam in my code?

Most deep learning libraries like TensorFlow and PyTorch have built-in support for Adam. You can simply call it when setting up your optimizer.