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What is Adam: Adaptive Stochastic Optimization about?

This document summarizes the Adam optimization algorithm presented in the paper "A Method for Stochastic Optimization". Adam is an algorithm for first-order gradient-based optimization of stochastic objective functions. It computes individual adaptive learning rates for different parameters using estimates of the first and second moments of the gradients. Adam combines advantages of AdaGrad and RMSProp by incorporating momentum and adapting the learning rate for each parameter. Empirical results show Adam w

Author

Ahmad Karlam

Language

EN