I never said I didn’t remember how to differentiate multivariate functions - my point was that equating conceptual mathematical knowledge and the ability to implement a specific application of such concepts in a time-constrained and stressful situation is inappropriate.

A lot of things need to come together in answering a question like this - remembering that the chain rule is the key concept in backprop the first place, knowledge of how to implement matrix algebra in code, knowing the commonly-used loss functions, how to compute their derivatives, and how to represent the differentiation in code, etc. None of these things is complicated on its own; the difficulty arises in bringing everything together in a small amount of time. It’s fair to expect people in the field to intuitively remember what is going on but on the spot implementation in under 30 minutes requires a level of rigor that is unrealistic for even a competent person who does not have the theory fresh in their memory.

You keep using the term ‘smart’ and I don’t know what you mean by this. Your last statement is just an assertion without argument, one you’ve repeated throughout your comments but I see no reason to believe, given the above.

The human brain doesn’t work like this. It’s not a question about “being smart” or simply having learned something previously. In order to perform an implementation of this on the spot in a stressful situation, the relevant theory needs to be very fresh in your memory.

I highly doubt you would be able to reproduce a proof of the Fundamental Theorem of Algebra on the spot, even though it’s a simple concept that many people learn in middle school.

I would probably fail this question because I haven’t worked with deep learning much since I graduated 4 years ago. I majored in math at an Ivy League school and graduated with a pretty good GPA, so I don’t think my math is ‘weak’, either.

This kind of question does not make sense to ask on a live call unless someone claims to be working with deep learning architectures as part of their daily work.