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Comment by namank | Hacker News Reader

Speaking for the other side, the point is not that you achieved the accuracy but it is understand how you thought about the problem. The ability to decide on a useful hypothesis, formulate the problem around it, and have some way to measure your progress is very very valuable from the employer's perspective.

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Sure, my point was more that this can be demonstrated by a much simpler project. How many candidates have you seen who haven't had a deep learning job before complete such a complicated project?

If you:

- have good problem solving and coding skills - spend a month or two learning how to build networks using good libraries

Then you will be able to get a good result with the Twitter task I pointed out. It takes being able to work input data correctly, think about what matters and what doesn't, then synthesize using readymade DL tools, usually in python. None of that is complicated esoteric neural net stuff, it is just motivated problem solving.

I'll add that as a big point — you should be able to code, problem solve, and be motivated.

The important part to mention is that the expected completion time of your project is 1-2 months. I have extensive ML experience (no deep learning though), and I think this project would take me at least 2 months to do well. This is a long time, especially for a beginner side-project. It is good to tell people to do side-projects, but suggesting projects like this is de-motivating for beginners. If someone on your team (who is presumably an expert already) does this side-project I'd be very interested in the results, and on how long it took them - it would make a good blog post. Your proposed beginner side-project should be less complicated than a published paper with 200 citations (the paper didn't use a nn).

Everyone thinks they can download tensorflow, hook it up to some stock market or Twitter api, hack a system in a week, and become a ML engineer. It is simply not this simple. It takes many months or years to achieve this experience.

Sometimes software engineers need to accept they aren't the smartest ones anymore. This is why they can't get the "smart" and "cool cutting edge" ML/DL jobs.

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