What is the impact of different optimizers on training neural networks?

What is the impact of different optimizers on training neural networks?

Different optimizers can significantly affect the convergence speed and final performance of neural networks. For example, Adam often converges faster than SGD.

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps an exponentially decaying average of past gradients and squared gradients.

What are the advantages of using SGD over Adam?

SGD can generalize better in some cases and is less memory-intensive, making it suitable for large datasets.

Are there specific scenarios where one optimizer is preferred over another?

Yes, for instance, SGD is often preferred for training large-scale models, while Adam is great for smaller datasets or when training speed is crucial.