I went back to school for cal 2, Multivariable calculus, Differential Equations and Libear Algebra. Took ML class from a local university after that.
Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
101–110 of 142 posts
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#102Earlier quoted context omitted.
> Stop focusing on MOOCs and youtube videos and study textbooks. I'd be ecstatic if I never again see a comment about how folks suddenly and completely understand a class they failed years ago after watching a 3blue1brown video.
Why does everyone act like resources must exist in a vacuum? Use both, there complimentary to one another.
I'd love for people to use 3blue1browns videos in conjunction with reading a textbook.
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#103Earlier quoted context omitted.
> study textbooks. Do exercises. Treat it like academic studying This. Highly recommend Russel & Norvig [1] for high-level intuition and motivation. Then Bishop's "Pattern Recognition and Machine Learning" [2] and Koller's PGM book [3] for the fundamentals. Avoid MOOCs, but there are useful lecture videos, e.g. Hugo Larochelle on belief propagation [4]. FWIW this is coming from a mechanical engineer by training, but…
PGMs were in fashion in 2012, but by 2014 when Deep Learning had become all the rage, I think PGMs almost disappeared from the picture. Do people even remember PGMs exist now in 2019?
Fashion is relevant only if you want to approach it as a fashion industry.
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#104Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#105My background is that of an econometrician (ie quantitative economist), and I now work as a Research Engineer at one of the FAANG research divisions. I think the advice about getting in as a hardware engineer is solid. At my workplace, there's a ton of need for people working on specialized hardware for DL, and for people working on the software that works with it (optimizing compilers, etc). If you are looking to br…
> Then, it's just a matter of getting interviews Are you implying that, once prepared well enough, the contents of the interviews are simpler than getting actually noticed in the pile of applicants ?
You need to think like an interviewer - what can you reasonably make someone do in half an hour (plus time for chat and questions after)? Apart from being able to parrot deep learning theory, implementing things is tricky. Do you learn anything from making someone implement VGG in their pet framework? Training models also takes more time than you have to spare.
Much easier to quiz the applicant how they would solve a problem, or to discuss a previous project or paper they've published (or are interested in). Some people will find that much easier than whiteboard coding, others will hate it.
It really depends where you apply and if you want an applied or research role. Some places won't touch you unless you've got a publication in somewhere like CVPR. Others will go _hard_ on the stats questions. Other places want to see a strong Kaggle rank or some personal projects. It's really useful to have a portfolio here.
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#106This conversation might help https://twitter.com/suzatweet/status/1078446189593321472
So basically a Luddite?
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#107Maybe someone who actually works at FAANG can weigh in, but I would think that one of your best bets would be getting into one as a general SWE and then transitioning to AI/ML internally after a year. I recall Google even having some sort of internal program that encouraged this. Getting into Google is a moonshot, but it's possible to do so with no prior professional programming experience if you put in a ton of effo…
Random side note, but when is the 'FAANG' acronym going to die? MSFT is killing it, prob the top tech company around these days. Needs to be included in that list.
How may I ask is MSFT a top tech company?
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#108Earlier quoted context omitted.
Random side note, but when is the 'FAANG' acronym going to die? MSFT is killing it, prob the top tech company around these days. Needs to be included in that list.
I agree, Microsoft is probably the #2 top company in AI after Alphabet, should be included
How may I ask is MSFT a number 2 company in AI?
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#109Stop focusing on MOOCs and youtube videos and study textbooks. Do exercises. Treat it like academic studying, and you'll end up with a decent education. It's important, because it's often easier to make a thing work okay than to understand why it works, so you'll get false confidence working through a tutorial. But then you want to apply that to something else and it doesn't work quite right, you won't know why it do…
> textbook
I recommend Kevin Murphy's ML a probabilistic approach and Ian Goodfellow's Deep Learning.
Those are the books used in most of the ML courses I took in grad school.
There is also Chris Bishop's Pattern Recognition and Machine Learning, but I think it is less popular now, than it was before.
Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?
#110Earlier quoted context omitted.
> Then, it's just a matter of getting interviews Are you implying that, once prepared well enough, the contents of the interviews are simpler than getting actually noticed in the pile of applicants ?
You need to think like an interviewer - what can you reasonably make someone do in half an hour (plus time for chat and questions after)? Apart from being able to parrot deep learning theory, implementing things is tricky. Do you learn anything from making someone implement VGG in their pet framework? Training models also takes more time than you have to spare. Much easier to quiz the applicant how they would solve a…
Thanks, that is helpful advice.