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I currently work as a software developer and I am curious about machine learning research.

Does it work if I can spend some extra time to read papers/collaborate towards ML research, or is a Master's degree the only option? If yes, how can I network to find like-minded ML enthusiasts with whom I can collaborate?

I do not have any professional experience as a machine learning engineer/researcher. I have done an undergrad course in ML and a Coursera specialisation in deep learning.

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    Reading papers is a good way to get started. Are you thinking of applying to any MS/PhD programs in the near future? I'd try contacting faculty at your undergraduate or any prospective schools to try and get advice as to how to move forward on this in addition to learning more through papers or new ML frameworks.
    – Daveguy
    Jun 22, 2021 at 18:30
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    "how can i network to find like-minded ML enthusiasts." Depending upon where you are there might be after-work meet-ups nearby. Have a beer, meet others, learn new things. I'd say informally that the field, at this time, is particularly fertile for amateur researchers, just find an interesting niche and dive in! Jun 22, 2021 at 18:38
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    Some questions with the independent-researcher tag might be helpful for specific points as well.
    – silvado
    Jun 22, 2021 at 18:51
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    What are your goals? To work on an ML problem, to publish papers, to become a professor?
    – Justas
    Jun 23, 2021 at 13:39
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    @Justas, it is more towards reading papers and publish papers if possible.
    – AksTester
    Jun 24, 2021 at 18:53

3 Answers 3

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If your goal is to eventually publish in one of the top conferences (nips, icml, iclr), realistically your only viable route is to get a phd. Actually, one of the most famous AI researchers in the world, David Silver, was working full time in software development before starting his PhD at the age of 28. If you are serious about doing research and want to make it your career, getting a phd is more or less your only viable route.

Obviously this is a very big commitment and life change because you would have to take a major pay cut, probably relocate, and devote the next several years of your life to your phd research. If you're not ready to make such a big decision, I would suggest that you find a paper(s) you're interested in, that doesn't currently have a public github code, and implement that paper's method. This can get your some experience and visibility. You could also try to find some existing repository and try to improve/add to it in some way. Either of these options won't result in publications but can get you some very tangible experience that can look good on your cv. And unlike trying for an actual publication, this is something you could realistically make progress on while also working full time.

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    The first sentence is nonsense. You don't need any credential to publish. You need a PhD for a career in academia, most likely, but not for much of anything else.
    – Buffy
    Jun 23, 2021 at 10:07
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    @Buffy Correct, but ML research is extremely competitive and fast moving. I am highly doubtful of someone being able to produce a meaningful piece of research while only having an undergraduate degree, especially while also working full time. There may be a few people with only an undergraduate degree that have done meaningful ML research (one paper in particular comes to mind), but all instances I am aware of involve that person doing internship/study with top research groups (e.g. google deepmind/brain) and working with several prominent experts as part of that internship.
    – Taw
    Jun 23, 2021 at 13:31
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This might be rather narrow, but it could still achieve what you're after: work on a chess engine. The current strongest chess engines, Stockfish & Leela Chess Zero, are both open-source community-driven engines that use machine learning. If you join them, you'd have ready access to lots of experienced developers as well as the hardware to test your ideas. Plus you can work as much or as little as you want.

I don't know how often this kind of work leads to publications, but it's certainly research since you'd be pushing the envelope into the unknown, and if it comes to it you can cite it as research experience for whatever application you might write in the future.

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There are two paths to doing research.

The bottom-up approach starts by learning the basics, through such things as reading and coursework, maybe online. A masters might lie on this path, but note that not all masters degrees have a research focus. Courses do give you the basics and reading can get you closer to research. But some papers are difficult without having the basics. Authors can make a lot of assumptions about the knowledge of their readers.

The top-down approach starts with a problem that you find interesting and is likely to be interesting to others and then do "just enough" reading to understand the problem and design and carry out a solution. Along with reading you can discuss your problem with others who might have the background you lack, provided you can connect with them and get them to talk to you. Looking for possible extensions to existing papers might be a way to find a problem.

The first is sort of breadth-first and the second is like depth-first searching for help.

But, everyone on the planet at the moment, along with all their siblings, seems to want to be studying and researching in ML at the moment. It is among the hottest of the hot topics currently. It will be hard, using either approach to be successful since the ideas are "out there" accessible to all those others. So, there is almost certainly a ton of parallel research going on on a very large number of approaches. Getting to the front of this pack is going to be challenging unless you already have gone some way on one of those two approaches.

You find collaborators at conferences pretty regularly, but you have to read the papers and meet the authors. You also need something to add if you want to get a collaboration going. The bottom up approach (via coursework) gives you access to professors who might give you a boost.

My advice is to give one or the other approaches a try and see what you can do. But, keep flexible. Don't commit too much time or intellectual effort to a too-narrow approach. Keep your options open. Either of the approaches gives you skills, not only in ML but also for what might be the next-big-thing.

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