$G^{t} = \beta G^{t-1} + (1 - \beta) \nabla J^2(W^{t})$

$W^{t} = W^{t-1} - \frac{\alpha}{\sqrt{G^{t} + \epsilon}} \nabla J^2(W^{t})$

The same principle applies to the bias parameters

$G^{t}$ - helper matrix for the algorithm

$\beta$ - the term that helps us to decrease the matrix G(usually around 0.9)

$W^{t}$ - the parameters

$\alpha$ - starting learning rate(usually something around 0.1 or 0.01)

$\epsilon$ - it is just to avoid division by zero( usually around 1e-8 )