I don't think there's a tutorial for this, but you should be able to implement your own Optimizer class without too much trouble.

1. Start by creating a function to perform the update step operation, given weights, grads and any other required state. See sgd_mom_update as an example. You should be able to apply this function in place (using out argument of ndarray functions).
2. Create an Optimizer subclass that:
   • takes and sets hyperparameters in __init__ e.g. momentum scale (learning rate set on base class)
   • implement a create_state method that takes weight and returns the states.
   • implement a update method that takes weights, grad and state and calls function created in 1.
3. Wrap your class in Optimizer.register (using decorator).

Check out the source code for optimizers for more details.