What are some common issues with optimizers in deep learning?

What are some common issues with optimizers in deep learning?

Some common issues include vanishing gradients, slow convergence, and getting stuck in local minima.

How can I address the vanishing gradient problem?

You can use techniques like gradient clipping, batch normalization, or choose activation functions like ReLU.

What optimizer would you recommend for a deep neural network?

Adam is a popular choice due to its adaptive learning rate and efficiency in handling sparse gradients.

Are there any specific scenarios where SGD performs better?

Yes, SGD can perform better in scenarios with large datasets and when fine-tuning models.