  The Adam optimizer is the standard choice in deep learning applications. I
propose a simple explanation of Adam's success: it makes each parameter's step
size independent of the norms of the other parameters. Based on this principle
I derive Iso, a new optimizer which makes the norm of a parameter's update
invariant to the application of any linear transformation to its inputs and
outputs. I develop a variant of Iso called IsoAdam that allows optimal
hyperparameters to be transferred from Adam, and demonstrate that IsoAdam
obtains a speedup over Adam when training a small Transformer.
