Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm used in machine learning and deep learning for training neural networks. It combines the advantages of two other extensions of stochastic gradient descent: AdaGrad and RMSProp, by computing adaptive learning rates for each parameter. It is widely used due to its efficiency, low memory requirements, and ability to handle sparse gradients on large datasets.
Developers should learn and use Adam Optimizer when training deep neural networks, especially in scenarios involving large datasets or complex models like convolutional neural networks (CNNs) or transformers. It is particularly effective for non-stationary objectives and problems with noisy or sparse gradients, such as natural language processing or computer vision tasks, as it automatically adjusts learning rates and converges faster than many other optimizers.