In binary classification, gradient descent is used to find the minimum of the error function. With error function defined as the binary cross entropy $($:$(1-r^t)log(1-y^t)\}$ and $y^t=tanh(w^T{x}^t+w_0),$ derive the update for $w_j$ and $w_0,$ where $j0.$

Note: Given $y=tanh(),$ the derivative $\frac{\text{d}\text{e}\text{l}\text{y}}{\text{d}\text{e}\text{l}}=1-y^2.$