Stochastic Gradient Descent for Multi-Class SVM

I'm trying to compute the optimization problem for a multi-class SVM loss function with $$L2$$ regularization.

$$\displaystyle f(W) = \frac{1}{n}\sum_{i=1}^n\sum_{c\neq y_i} \max\{0,1-w_{y_i}^Tx_i+w_c^Tx_i\} + \frac{\lambda}{2}||W||^2$$ where $$W$$ is a $$k$$ by $$d$$ matrix (data having $$k$$ classes and $$d$$ features with $$n$$ examples)

The link below shows how to compute the gradient and apply to stochastic gradient descent for a usual svm. How do I apply this to a multi-class svm?

https://svivek.com/teaching/lectures/slides/svm/svm-sgd.pdf

My idea was to take the loss function for a random example $$i$$. Now this loss will be $$f_i(W) = \frac{1}{n} \sum_{c\neq y_i}\max\{0,1-w_{y_i}^Tx_i+w_c^Tx_i\}+\frac{\lambda}{2}||W||^2$$ and using the fact that the gradient is the sum of all gradients of $$f_i(W)$$ (i.e., $$\nabla f_i(W) = \frac{1}{n}(\sum_{c\neq y_i}\nabla (\max\{0,1-w_{y_i}^Txi+w_c^Tx_i\})+\nabla(\frac{\lambda}{2}||W||^2)$$), I will compute the gradient. But I don't know how to compute $$\nabla (\max\{0,1-w_{y_i}^Txi+w_c^Tx_i\})$$. Can anyone help?