RMSprop

RMSprop, or Root Mean Square Propagation, is an adaptive learning rate optimization algorithm designed to improve the training of deep learning models. It adjusts the learning rate for each parameter based on the average of recent magnitudes of the gradients, which helps to stabilize the learning process and accelerate convergence. This method is particularly effective in dealing with non-stationary objectives, making it suitable for training neural networks on complex datasets. RMSprop is widely used in various deep learning frameworks and is favored for its efficiency in handling sparse gradients and its ability to maintain a balanced learning rate across parameters.