What are the different types of optimizers used in machine learning?

What are the different types of optimizers used in machine learning?

In machine learning, optimizers are algorithms that adjust the weights of a model to minimize the loss function. Common types include Gradient Descent, Adam, RMSprop, and Adagrad.

Can you explain how Gradient Descent works?

Sure! Gradient Descent is an iterative optimization algorithm that calculates the gradient of the loss function and updates the model's parameters in the opposite direction of the gradient to minimize the loss.

What are the advantages of using Adam over other optimizers?

Adam combines the benefits of two other extensions of stochastic gradient descent. It computes adaptive learning rates for each parameter and is generally faster and more efficient in practice.

Are there any scenarios where I should avoid using Adam?

Yes, while Adam is powerful, it may not perform well on certain problems with sparse gradients or when fine-tuning pre-trained models. In such cases, traditional methods like SGD might be more effective.