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Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

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Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#81

Post by walshemj »

brett40324 wrote: post #67
robertAngst wrote: post #53

Earlier quoted context omitted.

I'm just trying to get my first programming job. I cant seem to get past HR. My resume has that I'm a Chem Engineer BS, Industrial MS, 7 years in engineering, 2 years of Electrical Engineering. The first page of my resume is my 10 years of non-career programming experience. Built a Dishwasher(embedded C++), full stack app(RN JS, Mysql PHP laravel), and smaller projects. I cannot get past HR. Every real life programme…

Off topic, but wanted to reply regarding "The first page of my resume is my 10 years of non-career programming experience." This must be part or most of the problem. Cut your resume down to 1 page, if possible. Include a meaningful cover letter catered to the opportunity and specific company youre applying to. Shove the last ten years stuff into the very end, and start that first page with your software knowledge and…

Why not use a pitch or capability cv? and ditch the traditional cv format

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#82

Post by gcb0 »

asimjalis wrote: post #79

I am curious: Why do people want to pivot? Is the work more interesting? Is the pay higher? Are there more opportunities? Is there some other reason?

bad management.

on FAANGs, the teams are usually huge, 100+ people doing what a nimbler company does with 3 or less, to the point the employees don't even see that, because the product is now broken into several pieces to give the illusion of complexity. middle managers then break it down further that engineers start being know as the "person that writes the java files in that one directory" and nothing else. This creates constant fear of becoming irrelevant. All while you see 2~10% pay raises while hearing about undergrads making the same you make now with "new tech du jour". This creates even more pressure.

And because this cycle (stagnate, fear, learn, relief) repeats often, engineers start to associate learning a new tech with happiness, just because it offsets the psychological fear for awhile.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#84

Post by jimmy-dean »

boltzmannbrain wrote: post #74
imh wrote: post #71

Stop 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…

> 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…

Oof those are all dense reads for a new comer... For a first dip into the waters I usually suggest Introduction to Statistical Learning. Then from there move into PRML or ESL. Were you first introduced to core ML through Bishop? +1 for a solid reading list.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#85

Post by macheen »

ryanjodonnell wrote: post #30
xenihn wrote: post #12

Maybe 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.

I'd replace Netflix with Microsoft.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#86

Post by macheen »

make3 wrote: post #66
ryanjodonnell wrote: post #30

Earlier 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

Microsoft Research does a lot in AI but don't forget FAIR, Nvidia, Baidu, and Amazon. Smaller companies like OpenAI are making strides too.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#87

Post by sampo »

boltzmannbrain wrote: post #74
imh wrote: post #71

Stop 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…

> 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?

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#89

Post by KidComputer »

sampo wrote: post #87
boltzmannbrain wrote: post #74

Earlier 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?

You'll find plate models, PGM junk, etc in modern papers on explicit density generative models and factorizing latents on such models.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#90

Post by magoghm »

boltzmannbrain wrote: post #74
imh wrote: post #71

Stop 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…

> 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…

I would also include some books about statistics. Two excellent introductory books are:

Statistical Rethinking https://www.amazon.com/Statistical-Rethinking-Bayesian-Examp...

An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/

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