Earlier quoted context omitted.
What does it mean for a lookup/hash table to be differentiable?
I'm not a ML expert but I know a bit about math. It's "differentiable" in the same way that e.g. the "jump function" ( Heaviside step function ) is differentiable (not as a function from real numbers to real numbers, but as a distribution ). It's derivative is the "point impulse function" ( Dirac delta function ), which, again, is a distribution , not a real function. Distributions are nicely defined in math, but can…
I'm not an ML expert either but I have taken graduate level courses and published papers with "machine learning" in the title, so I feel like I should be able to understand these things better. The field just moves so fast. It's a lot of work to keep up. Easy-to-digest explanations like this are underrated.