What are some common optimization techniques in machine learning?

What are some common optimization techniques in machine learning?

Common optimization techniques include gradient descent, stochastic gradient descent, and Adam optimizer.

Can you explain how gradient descent works?

Gradient descent is an iterative optimization algorithm that minimizes a function by moving in the direction of the steepest descent.

What are the advantages of using Adam optimizer?

Adam optimizer combines the benefits of two other extensions of stochastic gradient descent, providing adaptive learning rates and faster convergence.

Are there any drawbacks to these optimization techniques?

Yes, some drawbacks include the risk of getting stuck in local minima and the need for careful tuning of hyperparameters.