Note: It is an improvement on the Adagrad optimization technique.
RMSprop is an optimization algorithm used in deep learning. It addresses the limitations of traditional stochastic gradient descent (SGD) by adapting the learning rate for each parameter individually. It calculates the exponentially weighted moving average of the squared gradients and uses it to normalize the parameter updates. This normalization helps prevent the learning rate from diminishing too quickly and allows for faster convergence. RMSprop is particularly effective in scenarios with sparse gradients and has been widely adopted as an efficient optimization method for training deep neural networks. It helps overcome the challenges of choosing an appropriate learning rate and accelerates the optimization process.