Stochastic Gradient Descent (SGD) is an increasingly popular method for optimizing the training of machine learning models.
Gradient Descent itself is a method of optimizing and subsequently quantifying the improvement that a model is making during training.
SGD has become the most popular optimization algorithm for fitting neural networks. One configuration of SGD that is becoming dominant in new AI/ML research papers is the choice of the Adaptive Moment Estimation (ADAM, introduced in 2015) optimizer.
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