RMSProp, or Root Mean Squared Propagation, is an advanced optimization algorithm used in machine learning and deep learning to improve the convergence of gradient descent. It addresses the limitations of traditional gradient descent methods, particularly the issue of diminishing learning rates in non-convex problems. By maintaining a decaying average of squared gradients, RMSProp adapts the learning rate for each parameter, allowing for more efficient updates. This dynamic adjustment helps the algorithm to navigate complex loss landscapes effectively, making it a popular choice among practitioners for training deep neural networks. Its implementation is straightforward and widely supported in various machine learning frameworks.