What are the differences between various optimizers in machine learning?

What are the differences between various optimizers in machine learning?

There are several optimizers like SGD, Adam, and RMSprop. Each has its strengths and weaknesses depending on the task.

Can you explain how Adam optimizer works?

Adam combines the advantages of two other extensions of stochastic gradient descent. It computes adaptive learning rates for each parameter.

When should I use SGD instead of Adam?

SGD is often preferred for tasks where you want more control over the learning process, especially in large datasets.

What are the common pitfalls when using these optimizers?

Common pitfalls include choosing inappropriate learning rates, not tuning hyperparameters, and overfitting.