RMSprop

RMSprop is an optimization algorithm used in machine learning to improve how models learn from data. It adjusts the learning process by keeping a moving average of the squared gradients (changes) for each parameter, which helps prevent some updates from becoming too large or too small. This stabilization allows the model to learn more efficiently, especially in complex problems, by dynamically tuning the step size during training. Essentially, RMSprop makes the learning process smoother and faster by adapting the learning rate based on recent experience, leading to better and quicker convergence.