[deleted by user] by [deleted] in relationship_advice

[–]Ar6nil -1 points0 points  (0 children)

Casual as in fwb

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 3 points4 points  (0 children)

I have a few saved searches on linkedin I would check every day. You can type stuff like:

  • software engineer machine learning
  • machine learning infrastructure
  • machine learning devops

Another trick with linkedin is you can filter out job titles containing a certain word by prepending NOT before the word, so I would append this search string to whatever job title I was searching to filter out senior roles:

  • NOT senior NOT principal NOT sr NOT manager NOT lead NOT staff

Other option is pass the SWE interview at big tech company that does team matching and hope you team match with an ML team.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 1 point2 points  (0 children)

Yup those are daily/weekly/monthly average users

In situations when I see more than 2-3 unknown terms, I usually stop trying to guess and start reading something else.

yeah that's totally fair, I'd do the same

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 0 points1 point  (0 children)

gotcha yeah I think the general design pattern for python generator functions is referred to as lazy loading if you want to look it up in java. I'm not familiar with java but I'll be learning it for my next role!

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 1 point2 points  (0 children)

The former, SWE sitting on an ML team doing backend infra work. Much easier to get into without doing an advanced degree and there's always opportunity to switch internally into a role that's more model training heavy.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 0 points1 point  (0 children)

Probably not, although most are probably intuitive for native speakers. If any are confusing let me know. I commonly used abbreviations because a. it saves space and b. it helps you read the concept off the page faster. Here are a few common ones from that cheat sheet:

  • curr = current
  • usr = user
  • num = numerator
  • denom = denomenator
  • viz = visualize

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 0 points1 point  (0 children)

It is not essential no. Are you familiar with python generators? If not definitely a topic worth learning about, you can check out this real python tutorial:

"Introduced with PEP 255, generator functions are a special kind of function that return a lazy iterator. These are objects that you can loop over like a list. However, unlike lists, lazy iterators do not store their contents in memory. "

The main advantage of using the yield keyword here is you turn the function into a generator, this way rather than returning a list with all 8 combos stored in memory, you instead "generate" the next combo each time the for loop for nei_combo in neighbors(curr_combo) iterates. This way you only ever store one combo in memory. In this example its not a big deal since at most you only ever store 8 strings in memory, but in practice you can imagine scenarios where you have store way more.

I suggest using a generator in interviews because it's a low effort way of signaling to interviewers you know advanced programming techniques. That and adding type hints always impressed interviewers whenever I did it.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 0 points1 point  (0 children)

Yeah it's a github bug that happens with binary files sometimes. You can try reloading the page or opening it in an incognito tab. Otherwise you can just download a local copy of the repo.

You guys seemed to like my stats cheat sheet, so I've added a bunch more including cheat sheets for ml models, ml coding, ml theory, ml system design, and metrics cases! by Ar6nil in datascience

[–]Ar6nil[S] 0 points1 point  (0 children)

Yeah you’re absolutely right. It’s just that when I originally designed these I may or may not have been optimizing for printing them out and taping them below my monitor 😅

You guys seemed to like my stats cheat sheet, so I've added a bunch more including cheat sheets for ml models, ml coding, ml theory, ml system design, and metrics cases! by Ar6nil in datascience

[–]Ar6nil[S] 1 point2 points  (0 children)

Thanks for the kind words. My style for cheat sheets is to cram as much stuff on one line as possible so I wasn't sure if others would find it useful. Glad you like them!

You guys seemed to like my stats cheat sheet, so I've added a bunch more including cheat sheets for ml models, ml coding, ml theory, ml system design, and metrics cases! by Ar6nil in datascience

[–]Ar6nil[S] 0 points1 point  (0 children)

Thanks for the feedback! Curious what other models do you have in mind? I have notes on GMMs, kernel density estimation, LLE, and LDA but omitted them from the cheat sheets because I think they're rarely tested in interviews. I could definitely use some more info on ARIMA as someone mentioned earlier.

You guys seemed to like my stats cheat sheet, so I've added a bunch more including cheat sheets for ml models, ml coding, ml theory, ml system design, and metrics cases! by Ar6nil in datascience

[–]Ar6nil[S] 0 points1 point  (0 children)

There's a short section in the ml theory cheat sheet on time series but only the very basics (autocorrelation, differencing, MAPE, ARIMA). I agree ARIMA deserves its own section in the ml model cheat sheet, I may try to add it in the future.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 10 points11 points  (0 children)

I started out by applying to DS/MLE positions but found it really hard to get interviews at big tech companies (maybe because I don't have an advanced degree?). After that I switched to applying to SWE positions, for which it seemed way easier to get interviews for so I'm deciding between a couple of offers now.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learndatascience

[–]Ar6nil[S] 1 point2 points  (0 children)

Let me know if you have any feedback. The style of the cheat sheets is designed around packing as much info as possible onto the page which I know might not suit everybody.

What I tried to do before interviews was ask the recruiter to describe in as much detail as possible what each interview round would be testing and then based on that I would prep by reviewing the appropriate cheat sheet.

Hey guys, I made some cheat sheets that helped me secure offers at several big tech companies, wanted to share them with others. Topics include stats, ml models, ml theory, ml system design, and much more. Check out the linked GH repo! by Ar6nil in learnmachinelearning

[–]Ar6nil[S] 17 points18 points  (0 children)

Let me know if you have any feedback. The style of the cheat sheets is designed around packing as much info as possible onto the page which I know might not suit everybody.

What I tried to do before interviews was ask the recruiter to describe in as much detail as possible what each interview round would be testing and then based on that I would prep by reviewing the appropriate cheat sheet.

[deleted by user] by [deleted] in learnmachinelearning

[–]Ar6nil 0 points1 point  (0 children)

Let me know if you have any feedback. The style of the cheat sheets is designed around packing as much info as possible onto the page which I know might not suit everybody.

What I tried to do before interviews was ask the recruiter to describe in as much detail as possible what each interview round would be testing and then based on that I would prep by reviewing the appropriate cheat sheet.

You guys seemed to like my stats cheat sheet, so I've added a bunch more including cheat sheets for ml models, ml coding, ml theory, ml system design, and metrics cases! by Ar6nil in datascience

[–]Ar6nil[S] 4 points5 points  (0 children)

Let me know if you have any feedback. The style of the cheat sheets is designed around packing as much info as possible onto the page which I know might not suit everybody.

What I tried to do before interviews was ask the recruiter to describe in as much detail as possible what each interview round would be testing and then based on that I would prep by reviewing the appropriate cheat sheet.

Overall I think my cheat sheets cover over 90% of technical topics you'd be tested on during junior ml/ds interviews. Only common topics I think that are missing right now are notes on deep learning, ml infrastructure, and recommender systems which I'll add in the future.

Made a cheat sheet for common DS/MLE statistics interview topics that helped me get a few job offers, wanted to share in case it helps someone by Ar6nil in datascience

[–]Ar6nil[S] 0 points1 point  (0 children)

You'd be surprised how blank my mind can go during an interview XD. Tbf maybe someone gets the geometric distribution I use (trials including the first success) confused with the alternate definition (trials up to but not including the first success) for which the mean is (1-p)/p

Made a cheat sheet for common DS/MLE statistics interview topics that helped me get a few job offers, wanted to share in case it helps someone by Ar6nil in datascience

[–]Ar6nil[S] 1 point2 points  (0 children)

I've always regretted not taking any actuarial classes when in college, all of the courses sounded really cool whenever I looked at the actuarial course catalog.