"How to get a job in deep learning" would include:
- What specific topics will be asked during interviews
- What the interview question format is like
- How to prepare for the interviews
- How to get interviews without a PhD. What do you need to show competence in your self learned skills?
They want to see if you did cool stuff before you applied for the job. If you didn't then you won't get an interview, but if you did then you have a chance no matter what your background is. Of course, the question of "what is cool stuff?" comes up. If it is building small projects with a a little bit of success, that probably won't do it (it might work for larger companies, or companies that need light ML/DL performed). But if it is "built twitter analysis DNN from scratch using Theano and that can predict the number of retweets a tweet will get: here's te accuracy, here's a link to a my write up on it and here's a link to github for the code.".
Edit: added words similar to this at the end of the blog post.
Statements like this contradict what you are saying here - to really build a model that predicts number of retweets based on the content of the message (not something like the average number of retweets this user has) is very non-trivial. If your threshold of a side project is publishable [1], it is an unrealistic expectation.
- 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.
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.