Related question: how can I learn how to read the mathematical notation used in AI/ML papers? Is there a definitive work that describes the basics? I am a post-grad Engineer, so I know the fundamentals, but I'm really struggling with a lot of the Arxiv papers. Any pointers hugely appreciated.
Ask HN: Daily practices for building AI/ML skills?
121–130 of 135 posts
Re: Ask HN: Daily practices for building AI/ML skills?
#122Earlier quoted context omitted.
> I got a masters degree in ML at a good school. I will say there’s pretty much nothing they taught me that I couldn’t have learned myself. Are people actually going into masters degree to learn? I thought the whole point of paying for masters is just credentialism
Technically you could do it on your own. Practically speaking? I quit my job and went to school full time. I spent 2 years studying the stuff practically 10 hours a day. The only way this is socially acceptable is if you get a piece of paper at the end which says you did it.
> The only way this is socially acceptable is if you get a piece of paper at the end which says you did it.
That’s true. Or if you’re really rich I guess.
Re: Ask HN: Daily practices for building AI/ML skills?
#123Earlier quoted context omitted.
> I will say there’s pretty much nothing they taught me that I couldn’t have learned myself. That said, school focused my attention in ways I wouldn’t have alone, and provided pressure to keep going. I have a masters degree in computer science and took a fair share of ML graduate courses. That pretty much summed up what I was thinking. They basically forced me to sit and learn something I wouldn't have alone. Now-- I…
I hate to say it, but the diploma also matters. Having an MS next to your name means employers will give you the time of day that others won’t get. I really don’t like that this is the way things work, but it is.
Does it? I have an MS, but never put it next to my name. I'm doing well :)
Re: Ask HN: Daily practices for building AI/ML skills?
#124I never get these type of questions because it’s like, what are you trying to do? Just acquire skills for the sake of it?
Pooling experiences, to learn more efficiently
Re: Ask HN: Daily practices for building AI/ML skills?
#125On top of what people have said, I have a few suggestions.
One is to reproduce recent papers for which the data is available and especially if the source code is available. Don’t look at their source code initially but use it if you get stuck as a debugging method (my model isn’t converging, do they get the same gradients given the same data?)
Another is a fun idea to play with: sports data sets. Of course you have to like at least one sport but there’s lots of sports data out there that is easy to download in convenient formats (especially for baseball, where professional statisticians have been employed to do analysis since at least the 50s, but afaik all the major sports have good records these days) and you can go a long way with simple models. I’ve wasted a lot of time on the weekend coming up with fun baseball analyses.
Re: Ask HN: Daily practices for building AI/ML skills?
#126Earlier quoted context omitted.
Yes, I was leaning more towards the "personal project" idea as well, something around document understanding. I subscribe to the "learning by doing/immersion" philosophy as well (upto a large extent). The problem with projects is one's understanding tends to go more and more specialised, and collaborating/connecting with other ML engineers requires a broader knowledge base sometimes. Also, for giving advice and usefu…
Greg Brockman's blog[1] has few links on how he picked up ML. Another link at [2] describes the path Michal(blog's author) followed (though it's aligned to "how i got into ..."). Both these blogs walk through how they were able to get into the ML bits of things. They have bunch of links (ex: [3]). I think it'll help if you can get a job at a company who's main focus is ML, you'll talk to folks who are doing research…
Great resources, especially Brockman's blog makes the experiences so much acceptable, knowing that even the top people had to struggle to get going in ML
Re: Ask HN: Daily practices for building AI/ML skills?
#127I'd spend most of that hour a day using ChatGPT, Bard and other models. Learning how to effectively prompt an LLM is an enormous space in its own right - and there's no shortcut for it, you have to actively play with the things. I've been using them constantly for over a year at this point and I'm still figuring out new tricks and strategies all the time. Weirdly, knowledge of Machine Learning isn't actually that rel…
That's a unique suggestion. Any chance you could share your favorite resources around it? Also, since you seem to have accumulated experience/expertise, would be super happy to read about it from you as well. Thanks for the advise.
Re: Ask HN: Daily practices for building AI/ML skills?
#128coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
Re: Ask HN: Daily practices for building AI/ML skills?
#129Highly recommended video of Karpathy - https://www.youtube.com/watch?v=I2ZK3ngNvvI
essentially, don't get paralysed on designing the perfect path on how to invest time/energy. just focus on putting in the hours everyday.
Re: Ask HN: Daily practices for building AI/ML skills?
#130There are so many mentions of reading paper. Do papers like these exists for regular enterprise software devs like me who make apis in Dotnet/go, good knol of multiple major cloud tools, k8s etc, has developed couple of iOS apps. I can do my job but I always wanted to learn and understand more. Family circumstances mean I can't afford to quit my job or go to school.
For a software engineer I would rather recommend implementing from a reference software implementation. There are for example tons of "Model XX from scratch" in Python - which you can translate to Dotnet/Go.
Implementing code from a paper is almost its own skillset. Papers are often math heavy and they are information dense, with lots of references to exiting works. They are designed for communicating to other researchers in the same field/niche.