AdaDelta

AdaDelta is an adaptive learning rate optimization algorithm used in training machine learning models, particularly in deep learning. It builds upon the idea of AdaGrad but addresses its main limitation of decreasing the learning rate too quickly. By maintaining a moving average of squared gradients and parameter updates, AdaDelta allows for more stable and effective learning rates throughout training. This method is commonly used in neural network training, especially when dealing with large datasets or complex models, as it reduces the need for manual tuning of learning rates.