Adam

Adam (Adaptive Moment Estimation) is an optimization algorithm used in training deep learning models. It combines the advantages of two other extensions of stochastic gradient descent, AdaGrad and RMSProp, to handle sparse gradients on noisy problems. It is known for its efficiency in large-scale machine learning problems.