What are the differences between various optimization algorithms?

What are the differences between various optimization algorithms?

There are several optimization algorithms, each with its strengths and weaknesses. Common ones include Gradient Descent, Adam, and RMSprop. Would you like a detailed comparison?

Yes, please explain Gradient Descent and Adam.

Gradient Descent updates parameters based on the gradient of the loss function, while Adam adapts the learning rate for each parameter, making it more efficient in many cases.

Which one is better for deep learning?

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 is more effective?

Yes, in simpler models or when fine-tuning, Gradient Descent can be effective, especially with a well-tuned learning rate.