I've been reading Neural_Network_Toolbox_Users_Guide and I have a question about below section.
As what it said in 3-19,
In fact, the gradients and Jacobians for any network that has differentiable transfer functions, weight functions and net input functions can be computed using the Neural Network Toolbox software through a backpropagation process. You can even create your own custom networks and then train them using any of the training functions in the table above. The gradients and Jacobians will be automatically computed for you.
Could you explain this part in detail how we can only get the gradient of training functions?