Adaptive Moment Estimation
Adaptive Moment Estimation (Adam) is a stochastic gradient descent optimization algorithm that is commonly used to update weights in neural networks. Adam combines the advantages of two other optimization algorithms, AdaGrad and RMSProp, to provide an efficient and effective method for updating weights. The algorithm calculates an exponentially weighted average of past gradients and past squared gradients, and uses these values to update the weights. Adam also includes bias correction to account for the fact that the estimates of the first and second moments of the gradients are biased towards zero in the early stages of training. This helps to improve the accuracy of the weight updates and can lead to faster convergence.
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