Gradient Descent Optimizers

Gradient descent optimizers are algorithms used to train machine learning models by iteratively adjusting parameters to minimize a loss function. Current research focuses on improving their efficiency and robustness, exploring variations like adaptive learning rate methods (e.g., Adam, AdaGrad) and techniques to mitigate sensitivity to hyperparameter initialization (e.g., ActiveLR). These advancements aim to accelerate training, enhance model generalization, and reduce computational costs, impacting various fields from computer vision (using CNNs) to quantum machine learning.