AdaGrad

AdaGrad (Adaptive Gradient Algorithm) is an optimization method used in machine learning to improve model training. It adjusts learning rates for each feature based on how much they have been updated historically, giving smaller updates for features that have been adjusted a lot and larger updates for those less adjusted. This makes the training process more efficient, especially for sparse data where some features are rarely used. Essentially, AdaGrad helps the model learn more effectively by dynamically changing its step sizes, leading to faster convergence and better performance on complex tasks.