How much math is needed for studying deep learning?
Any suggestions for good resources on math for deep learning much appreciated
Luckily, the math involved is not so difficult. For deep learning specifically you should be comfortable with linear algebra and multivariate calculus, and for machine learning in general you should be familiar with probabilistic thinking.
"Mathematics for machine learning" [1] is a good introduction to these topics.
A solid understanding of the domain and data under investigation is much more important, as is being a decent programmer, knowing your way around data cleaning and data management and having a solid understanding the strengths and weaknesses of the different algorithms out there.
If you're interested the absolute best way to get started is just to start. Download a well studied data set like this one: http://yann.lecun.com/exdb/mnist/ Grab one of the dozens of tutorials that talk about how to approach this data set and follow along in something like scikit-learn or Flux.jl. You'll soon enough have gone from zero to having developed a tool that can recognize handwritten digits. From there you can just keep going.
I'd say: start studying linear regression and when you master it end to end move into more complex topics.
It will undoubtedly sound naive to some, but I've been preferring the ability to "drive the car" over how to build one from scratch. Using fastai and their book/videos I've been able to go from dropping calculus and quitting a web dev bootcamp to building an ML product in about a a year. (and would really be much fast if I didn't have repetitive strain injury)
GP, keep searching for your starting point.
Drive the car, learn to change the oil and brakes as you go. And if you're super interested, mod it and then make one.
The issue with machine learning is that you need enough GPU VRAM to load your dataset and then have to wait for a result being trying something else.
If you have too little VRAM, you get nothing done, but if your GPU is slow (GTX 1070 is about 2x faster than a GTX 1060) you will have to wait before learning something after trying something. The feedback loop for learning is better if you're able to iterate quickly. This is why you sometimes see GPU rigs with up to 4 GPUs that are not being used on the same task (so you can do more than 1 thing at a time)
[0] - https://timdettmers.com/2020/09/07/which-gpu-for-deep-learni...
Yann LeCun is called Yann Le Cun [0] in French, but "Le" and "Cun" are smashed together in English. Lafayette [2] is of course called La Fayette [3]. Du Pont becomes DuPont, and so on.
[0] https://fr.wikipedia.org/wiki/Yann_Le_Cun
[1] https://en.wikipedia.org/wiki/Yann_LeCun
[2] https://en.wikipedia.org/wiki/Gilbert_du_Motier,_Marquis_de_...
[3] https://fr.wikipedia.org/wiki/Gilbert_du_Motier_de_La_Fayett...
Yann LeCun spells his own name like that: http://yann.lecun.com/
[1] These are actually Celtic originally: https://en.wikipedia.org/wiki/Celtic_onomastics#Surnames
[No, Your Name can't possibly be pronounced that way] http://yann.lecun.com/ex/fun/index.html#gellman
[1]: https://www.kalzumeus.com/2010/06/17/falsehoods-programmers-...