Entry to a career in deep learning

Submitted by No-Celebration6994 t3_10y1r6r in deeplearning

Hello all,

I don't really see much info on this throughout the web so I figured I'd see if anyone knew anything here. I currently have a bachelor's in Data Science/Economics and completed the Deep Learning specialization on Coursera, and I'm building a portfolio of personal projects. I'm very interested in a career in deep learning/computer vision but I don't really know where to get started - I know the industry is moving very fast, but does anyone have ideas of where to get in? Any relevant job is asking for a PhD/Master's and 5+ years of experience. I'm obviously not expecting to land a job creating production-level code right away, but rather work in a relevant area/supporting group of those roles so I can be involved in the industry and maybe move into one of those roles as I gain experience. Does anyone have a lay of the land or any advice? Thanks.

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agentfuzzy999 t1_j7vu3c6 wrote

I:

  • got a job writing production code
  • graduated with a bachelors
  • just got another job writing production code

In the span of a year.

I think being involved in multi-person DL projects/competitions/papers to put on your resume, and having good interviewing skills is genuinely more important than knowing the latest and greatest models and algorithms. It’s the same as the rest of the SWE field.

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junetwentyfirst2020 t1_j7y3cro wrote

What do you want to be doing exactly at this job? It’s a semi broad field, even with the specification of computer vision. I’ve usually seen computer vision broken down into: Capture, Perception, and 3D Reconstruction.

Deep learning usually happens on the Capture and Perception parts of the pipelines, because 3DR is Geometry and linear algebra.

Is this what you want?

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No-Celebration6994 OP t1_j9bkj7z wrote

My instinct says perception, but I can’t say I’m all too familiar with the full pipeline. Do you have any resources where I could read up on it? Thanks so much

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nonamefhh t1_j7yy2z2 wrote

I recently graduated from my computer science master degree and got a job as an ML Engineer. I am not only interested in pure data science.

Here is how I did it:

  1. Realise university doesn't teach the necessary knowledge --> so I did loads of courses and read books to get a foundation.

  2. With that knowledge, I was able to get a student job in the ml field. Finding that job took loads of hours researching in job portals.

  3. I had a study project for which I was allowed to write my master thesis, including object detection.

  4. End of university: Be prepared to move your location. I almost never found an interesting (entry) job in my hometown. It isn't easy to find an entry job, as you said, but they exist. Basically, search on every available job platform. Do your homework and write an appealing application. If you get invited, be relaxed and turn the conversation towards topics you know well. But most importantly, I realised it isn't worth taking a job if you don't vibe with the people you speak to during an interview. You can learn your craftsmanship, but it is incredible hard to work with people you dont like.

Basically, you spend 2-3 months researching and applying to EVERY job you can find, which doesn't sound like they are searching for a unicorn. The best job descriptions are rather short and precise. If they search for a senior don't immediately go on. It is very hard for companies to find a senior. We lost a colleague recently and we already know that we won't find a replacement for at least a year ... and as you might guess the position got replaced by a junior.

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nonamefhh t1_j7yygkm wrote

Another tipp: Read job offers like they are jokes. The person to fill the job requirements never exist.

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