Adad

Adad, short for Adaptive Amount Gradient Descent, is an optimization technique used in training machine learning models. It adjusts the learning rate for each parameter individually based on how much that parameter updates during training. This means that parameters that change a lot get smaller updates over time, while those changing less get larger updates, allowing the model to learn more efficiently. Adad helps improve training stability and speed, especially in models where different parameters learn at different rates. It’s one of several adaptive algorithms designed to make machine learning training more effective.