You're right, that was my mistake - but I wouldn't say that it's extremely ambiguous in this case, I just wasn't consequent enough in reading the formula. > I

You're right, that was my mistake - but I wouldn't say that it's extremely ambiguous in this case, I just wasn't consequent enough in reading the formula. > I don't know PyTorch, but regular Python, Numpy, Sympy, etc. seem very similar to Julia in this instance. If you need PyTorch to record the operations for the backward pass later on I believe you need to use PyTorch versions of *, +, etc.: torch.mul, torch.sub, torch.add, etc. In Julia you can just use built in functions and let Flux handle the backward pass.