The Adam optimizer is an adaptive learning rate optimization algorithm that combines the benefits of two other popular methods: AdaGrad and RMSProp. It is widely used in training deep learning models because it adjusts the learning rate based on the first and second moments of the gradients, allowing for faster convergence and improved performance across various applications in deep learning frameworks.
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