Adam (Adaptive Moment Estimation)

Information

• Adam (Adaptive Moment Estimation) is a stochastic gradient descent optimization algorithm commonly used for training deep neural networks.
• It is an adaptive learning rate optimization algorithm that combines the advantages of both AdaGrad and RMSProp optimizers.
• The algorithm maintains an exponentially decaying average of past gradients and past squared gradients to compute the adaptive learning rate.
• Adam optimizer also introduces bias correction to the first and second moments of the gradients to prevent them from being too heavily influenced by the initialization values.
• It has been shown to work well in practice for a wide range of deep learning tasks, especially when dealing with large datasets and complex models.
• Some of the advantages of Adam optimizer are fast convergence, good generalization performance, robustness to noisy gradients, and ease of use due to its automatic tuning of learning rates.