51–60 of 157 posts
This 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…
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…
Maybe "eigenvectors" is a bad example, because it's a pretty foundational linear algebra concept. But there is a threshold where it stops being a test of foundational knowledge and starts being a test of arbitrary trivia, and favors who has the most free time to study and memorize said trivia.
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…
Earlier quoted context omitted.
> 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…
FizzBuzz (or equivalent) is actually great IMO. It weeds out the people who lied on their resume, without punishing the people who never learned CS because they were too busy learning things that were actually useful to DS, like statistics or data visualization.
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…
Maybe "eigenvectors" is a bad example, because it's a pretty foundational linear algebra concept. But there is a threshold where it stops being a test of foundational knowledge and starts being a test of arbitrary trivia, and favors who has the most free time to study and memorize said trivia.
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…
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…
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 from the intended purposes).
Earlier 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…
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…
I think I know the answer to this, but how bad should I feel for being a software engineer with little-to-no knowledge of deep learning. I suspect it's not bad at all since the software engineering field has split into a few camps, and mine - backend systems work - isn't in the same universe as the machine learning one, for the most part.
Earlier quoted context omitted.
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 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.
Wow, nice resource! Wish it had some sections about (deep) reinforcement learning and its algorithms. Looks like it is in the plan though.