With the back propagate error for each l=L-1,L-2,\dots,1 as
\delta_j^l = \sum_k \delta_k^{l+1}w_{kj}^{l+1}\sigma'(z_j^l),we update the weights and the biases using gradient descent for each l=L-1,L-2,\dots,1 and update the weights and biases according to the rules
w_{ij}^l\leftarrow = w_{ij}^l- \eta \delta_j^la_i^{l-1}, b_j^l \leftarrow b_j^l-\eta \frac{\partial {\cal C}}{\partial b_j^l}=b_j^l-\eta \delta_j^l,