RMSProp, which stands for Root Mean Squared Propagation, is an adaptive learning rate optimization algorithm commonly used in training deep learning models. It addresses some limitations of traditional gradient descent methods, particularly in scenarios where the loss surface has steep and narrow valleys. RMSProp helps to stabilize the learning process by adjusting the learning rates based on the moving average of the squared gradients, allowing for more flexibility compared to other methods like Adagrad, which can lead to premature convergence due to aggressive learning rate decay ²⁴.

The algorithm uses a decay factor, often denoted as (\gamma), to weigh the historical squared gradients. This allows RMSProp to maintain a balance between past and current gradients, preventing the accumulation of squared gradients from growing indefinitely, which can hinder convergence ²⁵. The typical parameters for RMSProp include the learning rate, momentum, and a small constant for numerical stability, which can be tuned for optimal performance ⁴.

If you have further questions about RMSProp or its implementation, feel free to ask!