Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm used for training deep learning models. It combines the advantages of RMSProp and Momentum by calculating adaptive learning rates for each parameter based on estimates of the first and second moments of the gradients.
Frequently Asked Questions
Why is Adam optimizer so popular in deep learning?▼
It requires little hyperparameter tuning, handles sparse gradients well, converges quickly, and adapts learning rates dynamically for different parameters.
What are the key hyperparameters of the Adam optimizer?▼
The learning rate (alpha), and the exponential decay rates for the moment estimates, typically called beta1 (momentum decay) and beta2 (scaling decay).
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationNeural network training, gradient-based optimization, and learning rate adaptation.
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