Here I disagree with you pretty strongly. Once someone is comfortable with differentiable programming it's much more obvious how to build and optimize any type of model.
People should be more concerned about when to use derivatives, gradients, hessians, Laplace approximation etc rather than worry about the implementation details of these tools.
Abstraction can also aid depth of understanding. I know plenty of people who can implement backprop, but then don't understand how to estimate parameter uncertainty from the Hessian. The latter is much more important for general model building.
Here I disagree with you pretty strongly. Once someone is comfortable with differentiable programming it's much more obvious how to build and optimize any type of model.
People should be more concerned about when to use derivatives, gradients, hessians, Laplace approximation etc rather than worry about the implementation details of these tools.
Abstraction can also aid depth of understanding. I know plenty of people who can implement backprop, but then don't understand how to estimate parameter uncertainty from the Hessian. The latter is much more important for general model building.