The Math behind Adam Optimizer | Towards Data Science
The Math behind Adam Optimizer | Towards Data Science
Why is Adam the most popular optimizer in Deep Learning? Let’s understand it by diving into its math, and recreating the algorithm.
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.