1) Written by people who has no experience in industry or they are not working on "real" machine learning jobs
2) They think the standard in industry is pretty low and any BS works. For example the concept of "lagrange multiplier" is missing from the book. One need this concept to understand training convergence guarantee.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
#32Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#33I'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?
https://www.deeplearningbook.org/
Also there are various courses and lectures but that needs time and effort. There is no short cuts like the book posted by OP.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#34Earlier quoted context omitted.
Do you have any books/material that can help the learner acquiring this deeper understanding?
I know one good reference. https://www.deeplearningbook.org/ Also there are various courses and lectures but that needs time and effort. There is no short cuts like the book posted by OP.
But, this is also what you will practically be doing.
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#35Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#36Earlier 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.
Odds are usually represented with a colon -- the base odds are 1:4 (20%), not 1/4 (25%).
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#37This 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…
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#38Edit:
It seems the overlapping text also occurs on some pdf readers: https://github.com/BoltzmannEntropy/interviews.ai/issues/2
Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#39Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions
#40This 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…
Likewise, in the military, the use of countersigns have been designed to make non-native speakers stand out - should the countersign be compromised. For example, in WW2, Americans would use "Lollapalooza", as Japanese really struggled with that word.