The Adaptive Gradient Algorithm, commonly known as AdaGrad, is an optimization technique designed to enhance the performance of gradient descent. Unlike traditional gradient descent, which applies a uniform learning rate across all parameters, AdaGrad adapts the learning rate for each parameter based on the historical gradients. This means that parameters with frequent updates receive smaller learning rates, while those with infrequent updates maintain larger learning rates. This adaptive approach allows AdaGrad to effectively navigate complex optimization landscapes, making it particularly useful for training machine learning models, especially in scenarios with sparse data or varying feature scales.