# How does backpropagation learn convolution filters?

I've understood how the backpropagation algorithm uses the partial derivatives of the weights to train a normal neural network. However, I cannot quite understand how the algorithm changes the filters. Is it in the same way i.e. does the backpropagation algorithm find the derivates of the filters?

$$w_{t+1} = w_{t} - \epsilon \frac{\partial \mathcal{L}(w)}{\partial w} \\$$