Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?
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
#12Earlier quoted context omitted.
When I was a mathematician it was pretty common to make jokes whenever we actually had to evaluate an integral, along the lines of 'think back to your elementary-school calculus...'
“integrate by parts, like you learned in middle school” tf middle school did you go to?!
It's a joke. Like, we joke that the more math you learn the less arithmetic you can do (ok, maybe that one isn't a joke).
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#13Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?
I think they're called research engineering roles or ML engineering
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#14Data science and ML interviews can be tough because it's very difficult to prepare for everything and cover all the theory. A lot of the value you add comes from knowing the theory so it's understandable to test it but it's still hard to prepare well. And you have a take-home and/or LC style problem(s) in addition to the theory interview.
The hard questions in DS/ML interviews I've received over the years aren't the theory questions (which I rarely get asked), but the trick SQL questions that often depend on obscure syntax and/or dialect-specific features, or "implement binary search" when I'm not in the mindset for that as that isn't what DS/ML is in the real world.
I think they're fine as long as you know the format and have an opportunity to prepare or just get in the right mindset for it. And some things (like binary search) should be easy to write anyway.
The SQL questions can also be a symptom of the type of job - Facebook's first data science round focuses a lot on SQL but that's because it's a very product/analytics/decision-making focused role without that much coding or ML. With data science you have to be more careful about these things when searching for a job; you can't just use the job title as a descriptor.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#15Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?
There are. But
1) the titles will vary a lot (software engineer, ML engineer, research engineer, data scientist etc.) which makes it hard to locate those jobs and to move in the job market in general
2) you still need a reasonable amount of theory (not necessarily too much statistics) to use the tools well. And in all likelihood you will be tested on it in some way during the interviews.
3) the interviews/job descriptions that don't emphasise the theory often will be for jobs where you get a title like Machine Learning Engineer but you focus more on the infrastructure rather than on the ML code
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#16I'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 that corresponds to computing the norm of a vector and asking them "what does this do". Or even worse, showing them some picture of some cross-validation scheme and asking them to name it. Yes, your candidates should be able to do this, but positive answers to these kinds of questions are nearly useless. These are the kinds of questions you get answers to by Googling.
It's far more critical to know what your candidate can do, practically. Create a hypothetical dataset from your domain where the answer is that they need to use an L1 norm. Do they realize this? Do they even realize that the distance metric matters? Are they proposing reasonable distance metrics? Do they understand what goes wrong with different distance metrics? etc. Or problems where they need to use a network but say, padding matters a lot. Or where the particulars of cross validation matter a lot.
This also gives you depth. "name this cross validation scheme" gives you a binary answer "yes, they can do it, or no they can't" And you're done. If you have a hypothetical dataset, you can keep prodding. "Ok, but how about if I unbalance the data" or "what if we now need to fine tune" or "what if the payoffs for precision and recall change in our domain", "what if my budget is limited", etc. It also lets you transition smoothly to other kinds of questions. And to discover areas of deeper expertise than you expected. For example, even for the cross validation questions, if you ask that binary question, you might never discover that a candidate knows about how to use generalized cross validation, which might actually be very useful for your problem.
The uninformative tedious mess that we see in programming interviews? This is the equivalent for ML/DL interviews!
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#17Earlier quoted context omitted.
“integrate by parts, like you learned in middle school” tf middle school did you go to?!
It's a joke. Like, we joke that the more math you learn the less arithmetic you can do (ok, maybe that one isn't a joke).
In my undergrad abstract algebra class our professor asked us a question about finding the order of a group that involved dividing 32/8 and we all just sat there for ten seconds before someone bravely ventured "...four?"
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#18Wow! Great resource! Thank you!
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#19Are there deep learning roles that focus more on software engineering and using the tools rather than having a deep understanding of statistics?
> having a deep understanding of statistics?
As someone with a strong background in statistics, please tell me where I can find DS jobs that require this.
For me and all my statistics friends in DS we find much more frustration in how hard it is to pass DS interviews when you understand problems deeper than "use XGBoost". I have found that very few data scientists really even understand basic statistics, I failed an interview once because an interviewer did not believe that logistic regression could be used to solve statistical inference questions (when it and more generally the GLM is the workhorse of statistical work).
And to answer your question, whenever I'm in a hiring manager position I very strongly value strong software engineering skills. DS teams made up of people that are closer to curious engineers tend to greatly outperform teams made up of researchers that don't know you can write code outside of a notebook.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#20I'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…
Do you have any books/material that can help the learner acquiring this deeper understanding?