How do different optimization algorithms compare in Python ML?

What are the main optimization algorithms used in Python for machine learning?

The main optimization algorithms include Gradient Descent, Adam, RMSprop, and Newton's Method. Each has its strengths.

Can you explain how Gradient Descent works?

Gradient Descent minimizes the loss function by iteratively moving in the direction of the steepest descent, determined by the gradient.

How does Adam optimizer differ from traditional Gradient Descent?

Adam combines the benefits of two other extensions of SGD: AdaGrad and RMSProp. It adapts the learning rate for each parameter.

Which algorithm is generally the best for deep learning?

Adam is often preferred for deep learning due to its adaptive learning rates and efficiency in handling sparse gradients.