This Stanford course seems really advanced and intensive

I agree. Someone mentioned there weren't prereqs, I think, but man I was totally lost in the first class.

Oh yes, there are, anyone who says there are not is fooling others, or assuming you're a CS grad.

Are you talking about the submission course or Stanford?

Because There is a list of pre-reqs for the submitted course [1] and to be honest I feel like they are the standard requirements for you to fully understand DL may except signal processing stuff that might be taken as optional.

[1] https://fleuret.org/dlc/#information

You will have to know the basics of calculus, linear algebra, and probability. A few months of study.

If you're interested in Deep Learning or any area of ML it's fairly safe to assume you have a background in linear algebra, probability, calculus and obviously some basic programming.

If you're not interested in learning these areas, it's also safe to say you aren't really interested in deep learning either. Which is not to say if you don't already know these areas you aren't interested in deep learning, but if you don't know them and are interested in deep learning you're likely already studying them.

I say this because deep learning and the vast majority of ML really just boil down to an application of these basic tools. Deep learning/ML without the linear algebra, probability theory, calculus and coding isn't really anything at all.

> background in linear algebra, probability, calculus

Curious. What does 'background' mean in this sentence. You can spend years studying just one of these in depth. How much is "enough" for ML?

The basics. 1 semester course for each.

thanks! I wonder if someone has compiled a resource with just enough math for ML.

I believe fast.ai does this - they start with you using real tools and teach you the foundation as it becomes relevant

I'd say slightly more. Maybe it's just because I attended a state school, but I think my first semester calculus class was all single variable (20 years ago now, so my memory is rusty). You really to understand gradients and jacobians for ML, which I think was calc III for me. But you can skip curl and div part I guess.

Oh, that’s right. Multivariable was Calc II for me so technically two semesters.

how much of that do you really use for ML

Not much of your calculus book will be relevant beyond the first couple of chapters, but you'll live and die by the numerical-method sword. The idea is that you need the analytic insight from the former to understand the latter.

I don't know if I really buy that, though.

I’ve taken uni level courses for all of that but it’s been… 15+ years and is entirely out of my brain. I appreciate the feedback. Sounds like I need to brush up and I appreciate your point about it being a mathematical concept at its foundation

Machine Learning is broader than Deep Learning.

The Stanford course doesn't go as deep as, say, transformers.

Any courses you’re aware of that do?

Yes the posted one contains them (fleuret.org).

Also recommended: https://karpathy.ai/zero-to-hero.html

Oh good. Sorry I thought the implication was that this one fell short (as some of the other comments seem to suggest).

Thanks!

It's actually one of the more approachable ones you'll find.

Sorry but them there the facts. This stuff is hard. Otherwise it probably would have been done in the 1950s

IMO the only reason it wasn't done in the 1950s (noting that a lot of it in fact was done) was the widespread shortage of GPUs at the time.

Those folks were not exactly morons, they were just trying to build a nuke out of leather and driftwood.