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
#2This is amazing. I am ecstatic.
I've been looking for something exactly like this – and it's executed better than I could have imagined.
(Needs a good proofreader still, though! Also, whatever custom LaTeX template the authors are using is misbehaving a bit in various places. Still great content.)
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#3Fisher Information is under the "Kindergarten" section?
Maybe I've just been interviewing at the wrong places, I'd be very curious if anyone here has been asked to even explain Fisher information in any DS interview?
It's not that Fisher information is a particularly tricky topic, but I certainly wouldn't put it as a "must know" for even the most junior of data scientists. Not that I wouldn't mind living in a world where this was the case... just not sure I live in the same world as the authors.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#4Fisher Information is under the "Kindergarten" section? Maybe I've just been interviewing at the wrong places, I'd be very curious if anyone here has been asked to even explain Fisher information in any DS interview? It's not that Fisher information is a particularly tricky topic, but I certainly wouldn't put it as a "must know" for even the most junior of data scientists. Not that I wouldn't mind living in a world w…
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...'
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#5Wow, nice resource! Wish it had some sections about (deep) reinforcement learning and its algorithms. Looks like it is in the plan though.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#6This book has fun problems! Example:
During the cold war, the U.S.A developed a speech to text (STT) algorithm that could theoretically detect the hidden dialects of Russian sleeper agents. These agents (Fig. 3.7), were trained to speak English in Russia and subsequently sent to the US to gather intelligence. The FBI was able to apprehend ten such hidden Russian spies and accused them of being "sleeper" agents.
The Algorithm relied on the acoustic properties of Russian pronunciation of the word (v-o-k-s-a-l) which was borrowed from English V-a-u-x-h-a-l-l. It was alleged that it is impossible for Russians to completely hide their accent and hence when a Russian would say V-a-u-x-h-a-l-l, the algorithm would yield the text "v-o-k-s-a-l". To test the algorithm at a diplomatic gathering where 20% of participants are Sleeper agents and the rest Americans, a data scientist randomly chooses a person and asks him to say V-a-u-x-h-a-l-l. A single letter is then chosen randomly from the word that was generated by the algorithm, which is observed to be an "l". What is the probability that the person is indeed a Russian sleeper agent?
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#7Data 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.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#8This book has fun problems! Example: During the cold war, the U.S.A developed a speech to text (STT) algorithm that could theoretically detect the hidden dialects of Russian sleeper agents. These agents (Fig. 3.7), were trained to speak English in Russia and subsequently sent to the US to gather intelligence. The FBI was able to apprehend ten such hidden Russian spies and accused them of being "sleeper" agents. The A…
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%
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#9Data 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.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#10Fisher Information is under the "Kindergarten" section? Maybe I've just been interviewing at the wrong places, I'd be very curious if anyone here has been asked to even explain Fisher information in any DS interview? It's not that Fisher information is a particularly tricky topic, but I certainly wouldn't put it as a "must know" for even the most junior of data scientists. Not that I wouldn't mind living in a world w…
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?!