Скачать или смотреть AdaGrad (Adaptive Gradient Algorithm) Optimizer

AdaGrad (Adaptive Gradient Algorithm) is an optimization algorithm designed to improve the learning process in machine learning models, particularly in handling sparse data. Unlike traditional methods that maintain a constant learning rate throughout the training process, AdaGrad adapts the learning rate for each parameter based on the frequency and magnitude of its updates. It squares the gradients and accumulates them in a term called the "gradient accumulator." The learning rate is then scaled inversely proportional to the square root of this accumulator. This approach allows for smaller updates for frequently occurring features, leading to more efficient and robust learning, especially in scenarios with high-dimensional or sparse data. However, AdaGrad's continuous accumulation can lead to an overly aggressive decrease in the learning rate, sometimes resulting in premature and suboptimal convergence.