[pcbro141]

Tangent, but has anyone taken Fast.ai or similar courses and transitioned into the Deep Learning/ML field without a MS/PhD? To be honest, I don't even know what 'doing ML/DL' looks like in practice, but I'm just curious if a lot of folks get in to the field without graduate degrees.

[mustafa_pasi]

You can learn all you need to know in 2 to 3 university level courses. So we are talking less than a year of university courses.

Fast.ai is too high level. I don't like it. You would be better served taking actual university courses. A few days ago people linked to LeCun's university class[1]. This is a solid introduction. Does not cover everything but that is OK. Seems like it is missing Bayesian approaches. Then if you want to specialize in vision or speech or robotics or whatever, you take special classes on that topic and learn all the SOTA techniques. Then you are ready to do research already, or apply your knowledge to build stuff. Of course you still have to learn how to do real machine learning, which involves all the data manipulation stuff, but that is learned by doing.

[1] https://cds.nyu.edu/deep-learning/

[tracyhenry]

Another one I really liked is Berkeley CS182: https://cs182sp21.github.io/

The youtube playlist is here: https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjP...

Prof. Sergey Levine is REALLY good at explaining the intuitions of DL algorithms. This class also includes lectures on ML basics and very approachable assignments.

Many classes/blog posts start with describing what a neuron is - that IMHO is a super terrible way to teach a beginner.

To understand DL, one should know why we need activations (because linear models are not enough), why we need back-propagation (because we are optimizing a loss using SGD). This class is very great at explaining those things in an intuitive way. Following through I felt I built a pretty solid ML/DL foundation for myself.

[tmabraham]

I took the fast.ai course and now I am doing a Ph.D. in Biomedical Engineering focused on applying deep learning to microscopy.

I don't think fast.ai is enough if you want to do theoretical research in deep learning, but it certainly provides enough to work on practical problems with deep learning. That said, many of us in the fastai community are able to delve deep into, understand, and implement recent deep learning papers and even develop novel techniques. So I think with a little extra studying, one could go easily transition to core deep learning research.

[ShamelessC]

I'm not "in the field" yet; and I didn't take any courses I just kinda "dove in" on contributing to some open source repos because I've been a python dev for like 4 years now.

The pytorch codebase for, say, a transformer (a deep learning architecture which makes use of "attention") - is still not something I've yet grokked. I have however been able to pitch in with bug fixes as I continue learning and getting to that point.

This is how I would hope an entry-level position would be at a job. At some point companies have to realize education is just a part of it and that it takes time; particularly when things change this fast. I have no real-world clue though unfortunately.

Anyway, working on machine learning with vision is the first time I've actually felt like my work was exciting. The "result" you get is so much fun and working together with people given the proper culture is presumably a fantastic experience. I just (personally) can't get excited about using my code to write CRUD/frontend anymore. Not to imply those are the only two options; but that's been the case for me until recently.

[akgoel]

I am in a Fintech boot camp, and it’s clear that doing ML/DL requires very little math, as the math is all abstracted away.

[catillac]

The problem with this view is that once one gets stuck, which is very quick when one is doing the work for real, one doesn’t have any tools to debug anything except at the most basic level and most probably doesn’t understand anything intuitively enough to even reason about what the underlying problem could be.

I don’t do this work myself, but we’ve hired many interns from bootcamps to do ML, and ones from college with ML projects. The bootcamp grads with no additional background have almost universally hit hard walls once anything gets more complex than using Keras to glue together layers. It’s given me the impression, anecdotally, that bootcamps are largely predatory to take ones money and provide only a veneer of knowledge in the area. This doesn’t seem to apply to people with a CS or math background that took an ML bootcamp to add that dimension to their already-mathematical skillset. But people who have, again only anecdotally in my experience with an n of perhaps only 20, taken a bootcamp to reskill from a totally unrelated and perhaps qualitative field have not had success with a bootcamp alone, but have had success in doing what the above poster recommended in taking university courses in the area.

Very respectfully, if you’re in a boot camp right now, you’re unlikely deep enough into the day to day work of ML to make the assertion you’re making.

[maxwells-daemon]

I think it depends! If you want to zoom out and take the "systems view" using standard components, then you probably don't need much math. If you want to develop new architectures or algorithms, then you definitely will. The well-trodden paths of ML might have most of their math abstracted away, but in my experience every time you get close to the frontiers, people are using math to understand what's going on or develop new approaches.

[hogFeast]

It also doesn't really work if you have to tackle a new problem.

I stopped studying maths well before university. I am not some kind of math super genius. But working on my own stuff, which did involve new problems, I was up the creek fairly quickly without a solid mathematical understanding of the techniques I was trying to use.

I don't think the bar is particularly high here. Solid understanding of stats, ESL...but I have seen people shotgunning models (I did this years ago too), and that isn't going to work very long.

Also, I don't really understand why you wouldn't study some of this stuff. Maths as taught in schools treats you like a meat calculator...that isn't fun. But if you are interested in ML, going through Stats, Linear Algebra...it is pretty interesting because there are so many clear connections with your work.

[TrackerFF]

One example I can come up with now - image classification / segmentation / regression problems.

Unfortunately, not all data is available or provided in a data "friendly" format - sometimes all you get are image files, and similar. Maybe you want to read some value off these images, count objects, or whatever - which traditionally has been done by trained/skilled workers.

With CNNs, it _can_ be a trivial task implement models for solving the above problems. That's time and money saved for a business.

[maxwells-daemon]

Not Fast.ai, but I self-studied ML during undergrad (mostly from books) and am currently working as an ML research scientist.

That being said, I'm also thinking about starting an ML PhD because it does honestly open more doors to top research groups.

[p1esk]

ML PhD because it does honestly open more doors to top research groups

Correction: not ML PhD by itself - publications in top conferences open doors. Looking at the acceptance rates, I'm guessing most people with ML PhDs don't have such publications.