When Theoretical ML will catch up to Practical Deep Learning if it ever happens?

I am no expert in theoretical ML and statistical learning theory, but I heard from couple of sources that Practical Deep Learning has been developing way faster than theoretical ML, so many questions remain unanswered in low level calculus equations while Deep Learning Practitioners use their intuition to fill that void and to push the progress in DL forward. I think nobody right now can explain in theoretical terms why neural networks generalize so well while having more parameters than …

calculus deep learning deeplearning expert faster low practical questions statistical theory will