Is it important to have sharp math knowledge for deep learning?

Question

So I've been learning deep learning from Andrew Ng courses and it's great. I actually remember a lot of algebra and calculus, as I used to be a TA in uni, but almost 10 years have already passed and I'm not that sharp anymore. For instance, I have to frequently review how to transpose a matrix, review derivate tables etc.Is it worth to really review everything and do people that do deep learning professionally have always amazing knowledge about math, or is it normal to dig it up when you need it?I could buy some books and go through them, but I'm not sure if it's worth the time given that I can invest on learning all the ML/DL things I don't know, which are many.

codingslave · Accepted Answer

Depends, itll probably help to get hired. If you are in a highly applied position, probably better to know different network architectures, and why they work in specific ways. For instance, Restnet versus VGG16, what was the innovation? Which optimizer might be better for the two? When to slow learning rate, when to speed it up? Theres an art to tuning and getting neural networks to work that goes beyond mathematical understanding. But some positions may require their employees to understand the archaic math behind machine learning, down PCA and its relationship to eigenvectors, methods of non differentiable optimization, etc

sgillen · Answer

My honest advice. If you don&rsquo;t already have a really good understanding of the math I recommend you focus on the more applied stuff (like everything coding sleeve mentioned). I fear many of the roles that actually would require you to review the math might be looking for PhDs or similar.You probably should at least know how to do the basic stuff in numpy or whatever though.