[galuggus]

I'm interested in deep learning. I'm worried that my lack of higher level math skills will limit my progress

How much math is needed for studying deep learning?

Any suggestions for good resources on math for deep learning much appreciated

[blackbear_]

Lack of math knowledge won't limit much as a practitioner, but will definitely make it harder for you to understand what is going on.

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.

[1] https://mml-book.github.io/

[person_of_color]

Any solutions manual for this?

[blackbear_]

Yes, but it does not look easy to obtain: https://www.cambridge.org/highereducation/books/mathematics-...

[person_of_color]

I'm really sick of that.

[dagw]

What is your interest in deep learning? Developing new and novel algorithms, or applying already existing algorithms to new and novel problems, domains and datasets? If it's the latter then high level math skills aren't that necessary.

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.

[enriquto]

I'm not sure I understand your concern. Why are you interested, specifically, in deep learning (as opposed to more general statistics and optimization techniques)? What makes you un-interested in the big picture of statistics and optimization?

I'd say: start studying linear regression and when you master it end to end move into more complex topics.

[raidicy]

There are very cool things you can do with DL. And the things you need to learn are not too difficult for basic NNs. Please don't dissuade someone's interests by saying they need the knowledge "from the ground up". Of course you will always have a "deeper" understanding any maths subjects if you start from the very beginning. But for me there was always so little context as to their use and application it was disheartening.

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.

[enriquto]

Of course, you can use deep learning algorithms without even knowing that you are using them. I do not want to disuade anybody from doing that!

Maybe I misunderstood the question, but the GP said they wanted to "study deep learning". I do not think that is feasible at all without some math knowledge.

[throwaway6734]

https://course.fast.ai/ and the associated book are very helpfulf if you're coming from a software engineering perspective

[amatic]

One option is starting to learn about DL from the big picture, then filling in the details as you go along, eventually going to higher math. I prefer this approach to the opposite where you first learn math foundation and then the implementation and use-cases. I liked the Fast.ai course.