[source] # RMS Prop An adaptive gradient technique that divides the current gradient over a rolling window of the magnitudes of recent gradients. Unlike [AdaGrad](adagrad.md), RMS Prop does not suffer from an infinitely decaying step size. ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | rate | 0.001 | float | The learning rate that controls the global step size. | | 2 | decay | 0.1 | float | The decay rate of the rms property. | ## Example ```php use Rubix\ML\NeuralNet\Optimizers\RMSProp; $optimizer = new RMSProp(0.01, 0.1); ``` ## References [^1]: T. Tieleman et al. (2012). Lecture 6e rmsprop: Divide the gradient by a running average of its recent magnitude.