61–70 of 157 posts
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I had an "implement binary search" interview once. I came away feeling like I was being interviewed for the wrong role. I don't understand how anyone could think that's an appropriate interview task for a DS position.
I'm an MLE and I get asked much harder questions than that. Implement a binary search seems ... fine?
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.
That's why there's so much iteration and feedback gathering (e.g. A/B tests) as a part of DS/ML, which incidentally is rarely a part of the interview loop.
Anyone who claims they can get a good model the first time they train it is dangerously optimistic. Even the "how it works" aspect has become more and more marginal due to black boxing.
I actually bought this as a physical book on Amazon. Naturally it came as a print-on-demand book. Unfortunately it has many problems in this format. E.g. the lack of margins makes it hard to read the end of sentences towards the gutter. Also some text is pushed into each other. Not sure what source file format you have to provide to Amazon, but it's certainly not the pdf provided in the repo. Edit: It seems the overl…
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…
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 exists is because people with years of profesional experience can't program.
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.
The "model is in place, but I have no clue what's doing and so it can fail without me understanding when and how is straw-man". Especially for supervised learning, that is, we have a label for data, it is immediately clear whether the output of the model is "bunk, useless, or even harmful". There is no "fail silently by design".
I have been working in the field for almost 20 years in academia and in industry and it is not that I starting every PCA thinking about eigenvectors and eigenvalues and if you ask me now without preparing what are those, I would be between approximately right and wrong. But I fit many, many very accurate models.
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I'm an MLE and I get asked much harder questions than that. Implement a binary search seems ... fine?
But it makes sense for MLE! IMO you should ask a stats or probability question in a DS interview.
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Binary search in particular is surprisingly tricky, which is precisely what makes it useful for telling if someone knows how to program. To a significant extent, though, you can cheat by studying binary search itself, which is a surprisingly beautiful thing. I like this formulation for finding the first index in a half-open range where p is true, assuming p stays true thereafter: bsearch p i j := i if i == j else bse…
> Binary search in particular is surprisingly tricky, which is precisely what makes it useful for telling if someone knows how to program . That's the problem. There are many other ways to do that without risking false negatives and annoying potential candidates (e.g. I would not reapply to places that have rejected me due to skepticism about my programming abilities and using tests blatantly irrelevant to day-to-day…
Wow, nice resource! Wish it had some sections about (deep) reinforcement learning and its algorithms. Looks like it is in the plan though.
RL is still kind of niche - the number of companies that ship anything using RL and the number of jobs that require it are both quite low.
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…
> 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…
On top of the fact that these problems are often poorly selected, poorly communicated, conducted under completely unrealistic time pressure, often as pile-ons (with 3-4 strangers as if just to add pressure and distraction), and (these days) over video conferencing (so you have to stare in the camera and pretend to make eye contact with people while supposedly thinking about your problem, on top of shitty acoustics), etc, etc.
It's just fucking ridiculous.
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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…
It will be rat race when there are so many interview books and courses and websites. It was a not rat race before 2005, when there were only two reasons that one can solve problems like Pirate Coins or Queen Killing Infidel Husbands: the person is so mathematically mature that such problems are easy for them; the person is so geeky that they read Scientific American or Gardner's columns and remembered everything they…
I don't think I'm a bad engineer, but I'm certainly not the rock star you absolutely need for your team, but when it comes to this kind of “cleverness” tests, I'm really really good.
I've had the “Queen Killing Infidel Husbands" (with another name) in an interview last year and I aced it in a few minutes, and I didn't knew about "Pirate Coins", but when I read your comment HN said your comment was "35 minutes ago" and now it says "40 minutes" which means I googled the problem, figured out the solution and then found the correction online to see if I was right in less than 6 minutes, and so while I'm putting my son to bed!
It's really sad because there are many engineers much better at there job than me who will get rejected because of pointless tests like this…