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Deep Learning Interviews book: Hundreds of fully solved job interview questions

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Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#81

Post by rightbyte »

jstx1 wrote: post #54
nerdponx wrote: post #49

Earlier quoted context omitted.

FizzBuzz (or equivalent) is actually great IMO. It weeds out the people who lied on their resume, without punishing the people who never learned CS because they were too busy learning things that were actually useful to DS, like statistics or data visualization.

I've actually been given fizzbuzz in a DS interview! Up to that point I thought that fizzbuzz was just a meme because it's obviously too easy.

I tried to make Fizzbuzz on a paper when I first heard of it, and it had a bug printing fizzbuzzfizzbuzz on 15.

If you want a correct program without a compiler/computer I don't think anything is too easy. Maybe like, "make a function returning the sum of two float parameters".

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#82

Post by fault1 »

mrfusion wrote: post #11

Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?

I would say on average MLE roles tend to be more SWEng heavy. But some roles are as much creating infrastructure as running the tools.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#83

Post by mattkrause »

uoaei wrote: post #53
mcemilg wrote: post #41

The ML/DS positions highly competitive these days. I don't get why ML positions requires hard preparations for the interviews more than other CS positions while you do similar things. People expect you to know a lot of theory from statistics, probability, algorithms to linear algebra. I am ok with knowing basic of these topics which are the foundations of ML and DL. But I don't get to ask eigenvectors and challenging…

In part because ML fails silently by design. Even if the code runs flawlessly with no errors, the outputs could be completely bunk, useless, or even harmful, and you won't have any idea if that is true just from watching The Number go down during training. It's not enough to know how to build it but also how it works . It's the difference between designing the JWST and assembling it.

I'm sure this happens, but do you think the problem is actually one of mathematical savvy?

My guess would be that more machine learning projects go off the rails for want of understanding the data or the {business, research} problem.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#84

Post by crate_barre »

jollybean wrote: post #78
vsareto wrote: post #56

Earlier quoted context omitted.

I can’t help but think there’s been a ton of filters used in the past to figure out if someone is deeply geeky, and we’ll continue to invent more in the future. It’s really looking like another rat race. Especially since there’s no central authority, every hiring manager has the potential to invent their own filter, and make it arbitrarily harder or easier based on supply and demand (and then the filter drifts away f…

But if there is an abundance of supply, the company has to use some kind of filter. Testing for geekyness and ability to solve tricky coding math problems, seems like a rational way to do that. If companies were starving for talent because 'nobody could pass the test' - it would be another thing. But they have to set the bar on something, somewhere. I can't speak to AI/ML but I would imagine it might be hard to hire…

There’s an abundance of supply of people with masters degrees in machine learning? How’s that possible? I thought this shit was supposed to be hard.

Has humanity just scaled way too hard or something, because if we’re having an abundance of supply in difficult cutting edge fields to the point where they also have their own version of Leetcode, then what hope do average people have of getting any job in this world?

Or, is it at all possible that companies are disrespecting the candidate pool by being stingy and picky?

Maybe the truth is gray.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#85

Post by vidarh »

vanusa wrote: post #69
barry-cotter wrote: post #64

Earlier quoted context omitted.

> But I don't get to ask eigenvectors and challenging algorithm problems in an ML Engineering position at the same while you already proof yourself with a Masters Degree and enough professional experience. People know pity passes exist for Master's degrees. You can't trust that someone actually knows what they should know just because they have a degree. Ditto professional experience. The entire reason FizzBuzz exist…

We aren't talking about FizzBuzz here; but rather the fashionable practice of subject people to 4-6 hours of grilling on "medium-to-hard" problems that you absolutely cannot fail, or even be slightly halting in your delivery on. And which can only be effectively prepared for by investing substantial amounts of time on by-the-book cramming. On top of the fact that these problems are often poorly selected, poorly commu…

I'm quite happy these places makes it so clear they're not places I would be happy to work. I always ask about the interview process and tell the recruiters I'm not interested if they expect really lengthy processes. I'm fine with things dragging out of they have additional questions after initial interviews, but not if their default starting position is that they need that.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#86

Post by mattkrause »

Aethylia wrote: post #26
kilotaras wrote: post #8

Earlier quoted context omitted.

Bayes rule with odd ratios makes it pretty easy. base odds: 20:80 = 1:4 relative odds = (1 letter/6 letters) : (2 letters / 8 letters) = 2/3 posterior odds = 1:4*2:3 = 1:6 Final probability = 1/(6+1) = 1/7 or roughly 14.2% Bayes rule with raw probabilities is a lot more involved.

Assuming that the algorithm is 100% accurate!

I was also distracted by the fact that you can't (usually) hear the difference between English words written with one 'l' and those with two consecutive 'l's.

"Voksal" and "Vauxhall" seem like they should each have six phonemes.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#87

Post by fault1 »

whimsicalism wrote: post #52
nerdponx wrote: post #45

Earlier quoted context omitted.

Maybe "eigenvectors" is a bad example, because it's a pretty foundational linear algebra concept. But there is a threshold where it stops being a test of foundational knowledge and starts being a test of arbitrary trivia, and favors who has the most free time to study and memorize said trivia.

Having recently completed an MLE interview loop successfully at a top company, I'm wondering where you are getting asked complicated linear algebra questions in interview?

Hopefully you aren't equating "eigenvectors" to "complicated linear algebra question".

But I agree, a lot of MLE roles don't get asked such things.

I think the OP's guide is closer to interviews I've seen for phd programs.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#88

Post by angarg12 »

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry.

Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hunting bugs.

If I have to jump through so many hoops when changing jobs I'll seriously consider a regular non-ML position.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#89

Post by whatshisface »

renewiltord wrote: post #71
whatshisface wrote: post #46

Earlier quoted context omitted.

A single letter is chosen randomly? Huh? Why would you do that?

Seems a bit pointless to ask. You want them to make up a story? "The data scientist's radio link degrades to static while he waits for the answer and all he hears is the letter 'l'". There.

It's just a bit funny to come up with a clever justification for 50% of the problem only to quit at the last moment with tacked-on math problem stuff.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#90

Post by kragen »

rightbyte wrote: post #81
jstx1 wrote: post #54

Earlier quoted context omitted.

I've actually been given fizzbuzz in a DS interview! Up to that point I thought that fizzbuzz was just a meme because it's obviously too easy.

I tried to make Fizzbuzz on a paper when I first heard of it, and it had a bug printing fizzbuzzfizzbuzz on 15. If you want a correct program without a compiler/computer I don't think anything is too easy. Maybe like, "make a function returning the sum of two float parameters".

That would just test syntax, though. Fizzbuzz tests logic. Your bug was a logic bug.

To a certain extent you can dispense with mental logic by using a compiler. But the feedback loop is much slower. Thinking your logic through before feeding it to a compiler is like looking at a map when you're driving a car; you can cut off whole branches of exploration.

Binary search is a particularly tricky logic problem in part because it's so deceptively simple. In a continuous domain it's easy to get right, but the discrete domain introduces three or four boundary cases you can easily get wrong.

But the great-great-grandparent is surely correct that many programming jobs don't require that level of thinking about program logic. Many that do, it's because the codebase is shitty, not because they're working in an inherently mentally challenging domain.

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