How do different optimization algorithms compare?

What are the main differences between gradient descent and Adam optimizer?

Gradient descent updates parameters based on the gradient of the loss function, while Adam uses adaptive learning rates and momentum.

Which one is generally better for deep learning tasks?

Adam is often preferred for deep learning due to its adaptive learning rates, which can lead to faster convergence.

Are there situations where gradient descent might outperform Adam?

Yes, in simpler problems or when fine-tuning, gradient descent can sometimes yield better results due to its simplicity.

Can you explain the concept of learning rate in these optimizers?

The learning rate determines how much to adjust the model's parameters during training. A high learning rate can lead to overshooting, while a low rate can slow down convergence.