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Deep Learning Interviews book: Hundreds of fully solved job interview questions
91–100 of 157 posts
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#92In my 20s, I was doing data science at a very high level spanning multiple disciplines. Truly state of the art. I would like to think I was quite good at my job.
I am 99% certain I would not have passed the interview bars set today. More specifically, the breadth they expect you to master is very puzzling (and seemingly unrealistic).
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#93Earlier quoted context omitted.
But it makes sense for MLE! IMO you should ask a stats or probability question in a DS interview.
The distinction between the two roles isn't that clear. Some data science jobs are very focused on engineering.
Agreed. MLE in very ML-heavy companies tends to mean SWE who work on ML systems, and sometimes, that can mean as much working on stuff like infrastructure as modeling.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#94The 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…
A reason for such requirements is similar to that that software engineers need to leetcode hard: supply and demand. Prestigious companies get hundreds, if not thousands, of applications every day. The companies can afford looking for candidates who have raw talent, such as the capability of mastering many concepts and being able solve hard mathematical problems in a short time. Case in point, you may not need to use…
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Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#95I've interviewed well over 100 people for DL/ML positions. This may be a good roadmap to what some people ask, but it's a terrible guide to what you should ask. It's like a collection of class exam questions. Just as in programming, the world is full of people who can recite facts but don't understand them. There is no point in asking what an L1 norm is and asking for its equation. Or say, giving someone the C++ code…
Exactly I though the same. Not sure what a really good alternative is. BUT you may be in risk to get bad candidates, since they might be the ones with the best intrview practice.
Maybe that kind of questions are ok for people without expirience but not for seniors.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#96Earlier quoted context omitted.
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.
Haha fair enough.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#97The 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…
A reason for such requirements is similar to that that software engineers need to leetcode hard: supply and demand. Prestigious companies get hundreds, if not thousands, of applications every day. The companies can afford looking for candidates who have raw talent, such as the capability of mastering many concepts and being able solve hard mathematical problems in a short time. Case in point, you may not need to use…
Given that PCA is heavily antiquated these days, I'd say that asking your candidates to know algebraic topology (the basis behind many much more effective non linear DR algorithms like UMAP) is far better... But in spite of the field having long ago advanced beyond PCA, you're still using it to gatekeep.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#98Earlier quoted context omitted.
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…
I currently work as an ML engineer and have interviewed on both sides for some well known companies.
The absolute demand in number of people is small compared to popularity. It would not surprise me at all if many computer science master's programs had a majority of the students studying machine learning. I remember in undergrad we had to ration computer science classes due to too much demand from students. I think school had 3x majors over a couple year time period in CS.
The number of needed ML engineers is much smaller than total software engineers. When a lot of students decide ML is coolest we have imbalanced CS pool with too many wanting to do ML. Especially when for ML to work you normally need good data engineering, backend engineer, infra, and the actual ML is only a small subset of the service using ML.
At the same time supply of experienced ml engineers is still low due to recent growth of the field. Hiring 5+ years of professional experience ML engineers is more challenging. The main place were supply is excessive is for new graduates.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#99Earlier quoted context omitted.
A reason for such requirements is similar to that that software engineers need to leetcode hard: supply and demand. Prestigious companies get hundreds, if not thousands, of applications every day. The companies can afford looking for candidates who have raw talent, such as the capability of mastering many concepts and being able solve hard mathematical problems in a short time. Case in point, you may not need to use…
Given that PCA is heavily antiquated these days, I'd say that asking your candidates to know algebraic topology (the basis behind many much more effective non linear DR algorithms like UMAP) is far better... But in spite of the field having long ago advanced beyond PCA, you're still using it to gatekeep.
The initialization strategy for UMAP is important enough that asking about that in practice is probably more important than anything out of Ghrist's book as an interview question
cf. https://twitter.com/hippopedoid/status/1356906342439669761
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#100Earlier quoted context omitted.
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 b…
Ye I meant running it and then correcting the error.
Concerning binary search I acctually implemented that in an ECU for message sorting. It took like a whole day, including getting the outer boundries one off too big in the first test run. Funnely enough the vehicle ran fine anyway.
I would never pull that algorithm off correctly in an interview without training to, I think.