tf.optimizers.Adam

Adam (Adaptive Moment Estimation) is an optimization algorithm used for training deep neural networks. It is based on the idea of computing adaptive learning rates for each parameter instead of a single learning rate for all parameters.

The Adam optimizer maintains a moving average of the gradients of each parameter and the squared gradients. It then uses these moving averages to update the parameters in a way that takes into account both the current gradient and the historical gradients. The algorithm also incorporates bias correction to account for the initial estimates of the moving averages being biased towards zero.

Adam is known for its convergence speed and is widely used in deep learning applications. It is available as part of the TensorFlow library as tf.optimizers.Adam.