The Adam optimizer is a popular optimization algorithm used for training deep learning models, combining the benefits of two other extensions of stochastic gradient descent. It adjusts the learning rate for each parameter individually, using estimates of first and second moments of the gradients to improve convergence speed and performance. This makes it particularly useful in various applications, including recurrent neural networks and reinforcement learning.
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