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The Math behind Adam Optimizer | Towards Data Science (towardsdatascience.com)

submitted 9 months ago by [email protected] to c/[email protected]

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The article discusses the Adam optimizer, a popular algorithm in deep learning known for its efficiency in adjusting learning rates for different parameters.

Unlike other optimizers like SGD or Adagrad, Adam dynamically changes its step size based on the complexity of the problem, analogous to adjusting stride in varying terrains. This ability to adapt makes it effective in quickly finding the minimum loss in machine learning tasks, a key reason for its popularity in winning Kaggle competitions and among those seeking a deeper understanding of optimizer mechanics.

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