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The Adam optimizer is an advanced optimization algorithm used in training deep learning models, combining the benefits of two other popular methods: AdaGrad and RMSProp. It adjusts the learning rate for each parameter individually, allowing for more efficient convergence during the training process. This method is particularly effective for large datasets and high-dimensional parameter spaces, making it a popular choice among practitioners.
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