Adam Optimizer

Adam (Adaptive Moment Estimation) is an optimization algorithm that computes adaptive learning rates for each parameter. It stores both an exponentially decaying average of past gradients (momentum) and an exponentially decaying average of past squared gradients (variance). The algorithm uses these values to dynamically adjust the learning rate for each parameter, providing faster convergence and better performance on problems with noisy or sparse gradients.

Computer vision model training, Natural language processing tasks, Deep neural network optimization, Training large-scale machine learning models