Well, not much. Data isn't the main product where I work for, so I end up just using python libraries for some mundane applications and small datasets (image classification and recommender systems). But I'm the "need to know from scratch" kinda person and I wonder if I should waste my time learning the math behind it or if its useless
which tells the secret of when to stop when you're doing "early stopping", something I've seen many modern deep learners fail to get right.
Off the top of my head I would say the fundamental math about deep networks is not really new. Most of the work in that field is pretty ad-hoc and not a lot is proven; probably people that are proving things are using difficult graduate-level math but you don't need to go there.
Comments
Well, not much. Data isn't the main product where I work for, so I end up just using python libraries for some mundane applications and small datasets (image classification and recommender systems). But I'm the "need to know from scratch" kinda person and I wonder if I should waste my time learning the math behind it or if its useless
What's the first derivative of
wolfram alpha told me its: cos(x) - xsin(x) lol kidding, i had calculus 15 years ago. so it sounds I should recap derivatives.
This book is a little old but you should learn whatever math it takes to understand it
https://www.amazon.com/Networks-Recognition-Advanced-Econome...
Another oldie-but-goodie is
https://www.amazon.com/Neural-Networks-Lecture-Computer-Scie...
which tells the secret of when to stop when you're doing "early stopping", something I've seen many modern deep learners fail to get right.
Off the top of my head I would say the fundamental math about deep networks is not really new. Most of the work in that field is pretty ad-hoc and not a lot is proven; probably people that are proving things are using difficult graduate-level math but you don't need to go there.
thank you Paul for the references, will def take a look mainly in the early stopping problem.