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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

#121

Post by bronxbomber92 »

angarg12 wrote: post #88

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry. Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hu…

This is the same for any specialized software engineering role. Compilers, GPGPU, embedded systems, computer graphics, image processing, etc. In an interview panel for any of these roles, you will be expected to be a competent software engineer and have domain knowledge about the sub-field.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#122

Post by throwaway2331 »

angarg12 wrote: post #88

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry. Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hu…

No post body was provided.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#123

Post by godelski »

dekhn wrote: post #106
angarg12 wrote: post #88

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry. Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hu…

there's a bunch of gatekeeping to get into ML. Part of it is that ML people don't want non-ML people to know just how much of what they do is drudgery and how little of it is exciting math, or have competition from people with similar skills. And those roles come with a lot of prestige. I went through all that and am a SWE again instead of an ML engineer. The one thing I learned from all that? "The very best models a…

I agree with you, but I also wish more ML people knew more math. Though I think there's a difference between research and production (I'm in research).

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#124

Post by coliveira »

light_hue_1 wrote: post #34
master_yoda_1 wrote: post #33

Earlier quoted context omitted.

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.

Yeah. You just have to build models, experiment, intentionally make bad decisions, and get a feel for how things work. There's no clear shortcut. But, this is also what you will practically be doing.

I make a point of never doing "projects" during a recruitment process. The fact is that there are too many good opportunities out there that will not require me two spend a full day or even several days in a week to complete a job application, without compensation. Whenever I hear that there is a "project" to complete, I just tell them that I already have tons of projects to work on and pass the "opportunity".

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#125

Post by godelski »

crate_barre wrote: post #84
jollybean wrote: post #78

Earlier 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…

> There’s an abundance of supply of people with masters degrees in machine learning? How’s that possible?

I don't know for ML, but there are almost 12k Masters CS degrees awarded per year and 1.1k PhDs. If my university is any indication, then there's a good portion of those that are ML or doing some sort of ML in their research. But even if it was just 10%, that's a lot of people per year that are being added. This is just the US btw.

https://datausa.io/profile/cip/computer-science-110701

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#126

Post by godelski »

Der_Einzige wrote: post #97
hintymad wrote: post #44

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…

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.

UMAP (and t-SNE) aren't the same as PCA. UMAP is pretty close to t-SNE and I think expanding PCA (Principle Component Analysis) and t-SNE (teacher Stochastic Neighbor Embedding) explain the difference. Neighbor embedding is a visualization technique and not the same as determining principle components. PCA preserves global properties while t-SNE and UMAP don't. They are good techniques for _visual_ dimensional reduction, but they aren't going to tell you the dominant eigenvectors of the data, or _dimensional reduction_. This is a bit of a pet peeve of mine.

There's some more in this SE post https://stats.stackexchange.com/questions/238538/are-there-c...

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#127

Post by light_hue_1 »

uoaei wrote: post #53
mcemilg wrote: post #41

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…

In part because ML fails silently by design. Even if the code runs flawlessly with no errors, the outputs could be completely bunk, useless, or even harmful, and you won't have any idea if that is true just from watching The Number go down during training. It's not enough to know how to build it but also how it works . It's the difference between designing the JWST and assembling it.

And you learn literally zero about a candidate's ability to understand when and why things work by asking questions about eigenvectors. Someone can understand what an eigenvector is and still not have any clue about how you figure out a system is working, why it's working, what is likely to happen in production, how you test the limits of your method's ability to generalize, how you take an real problem and find something that you can productively use ML on, etc.

People say things like "you need to know how it works" but "it" doesn't work using your knowledge of eigenvectors. If you want to test how "it" works, test that, literally. Put up a model on the board and a dataset. Ask people about what might happen when you apply one to the other. What changes they would make in response to changes in the data. What they would do in response to the following training curves, budget limitations, etc.

These interviews are terrible and they select for people that regurgitate facts.

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#128

Post by emotional_fool »

angarg12 wrote: post #88

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry. Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hu…

And most likely will be paid <= software-engineers

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#129

Post by xwolfi »

Jensson wrote: post #119
dekhn wrote: post #106

Earlier quoted context omitted.

there's a bunch of gatekeeping to get into ML. Part of it is that ML people don't want non-ML people to know just how much of what they do is drudgery and how little of it is exciting math, or have competition from people with similar skills. And those roles come with a lot of prestige. I went through all that and am a SWE again instead of an ML engineer. The one thing I learned from all that? "The very best models a…

Getting state of the art performance in ML requires a lot of intuition about equations though. I've seen some of the top ML engineers work at Google, they all have a really good understanding of math, how formulas translates into measurable results etc. An ML education or research background seems less important, if you have that from studying physics or math or anything then it still translates. I feel the biggest p…

And the same can be said of non-ML IT ! You always contrast better when you understand the whole history behind why you write something a certain way, even if you could just learn on the job seeing it over and over. It's like how they teach proper sorting by giving you all the bad ways first.

Also, it's not often but you do have to show creativity at times, to solve a new problem or something, and having an intuitive theoretical understanding goes a long way vs someone who learned via base mimicry.

I think instead of gatekeeping we could build bridges: be very clear that salary / responsibilities will be lower at first and judge on results. If an ML person is brilliant, he won't be threatened by an idiot Java dev. And if a Java dev is able to produce good results even if the way he reached them is less graceful, then an ML engineer should probably start shifting the second gear :D

Re: Deep Learning Interviews book: Hundreds of fully solved job interview questions

#130

Post by coconut_man »

angarg12 wrote: post #88

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry. Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hu…

just like Software Engineer having to pass leetcode round and system design round, I also doubt that ML theory and practice is much harder than system design (theory and practice). Beside that, you get paid more than Software Engineer.
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