Matrix Calculus for Deep Learning
I’m in favor of there being more and better resources to learn anything out there, but every time I see a deep learning 101 type material all I can think is “who is this for?”.In ~July 2016 I was at a presentation by NVidia at GW in DC. They showed off how easy it was to build out and train a model using some of their tooling (Digits maybe?). After the demo they opened it up for questions and a grad student ‘asked’ “You just did in 10 mins with 30 lines of code what I worked on for an entire semester”.That’s been the trajectory of the tools and increasing abstraction in this space. It’s just getting easier and easier to build models that work (which is great), and it gets easier and easier to do so without knowing more than an extremely high level overview of the math behind it all.So while this looks like a great resource - who’s it for?For jobs/problems that need you to have a thorough understanding of the math and theory behind the networks this isn’t going to cut it.For jobs/problems that need you to get something working math or not - this likely isn’t necessary to get started.So it’s for people that have been getting into DL but also haven’t bothered or needed to look up the math concepts?