The Adam optimizer is an adaptive learning rate optimization algorithm designed to improve the efficiency of training deep learning models. It combines the benefits of two popular optimization methods: AdaGrad and RMSProp, which makes it particularly effective for handling sparse gradients and non-stationary objectives. This optimizer has gained significant popularity due to its ability to converge faster and perform better in practice, making it a common choice for training neural networks.
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