Machine Learning, Gradient Descent, Data Visualization, Data Science.
slower rate. One way to address this is to set different learning rates for each feature, but this gets messy fast. AdaGrad addresses this problem using this idea: the more you have updated a feature already, the less you will update it in the future, thus giving a chance for the others features