That's a great question. I don't know the answer, but I have some ideas.
I live in San Francisco, so my experience is different from yours in many ways, but in other ways it is not. Even here in SF, arguably the capital of the tech scene, employers still care primarily about education and work experience. Additionally, despite the amazing progress we've seen in deep learning, most companies don't use it and the companies who do often rely more on traditional ML techniques since they're easier/more practical.
This leads me to some thoughts:
If employers want to see work experience and a degree, perhaps it makes sense to get some work experience and a degree? I would also try if possible to move to a country/city with more opportunities, and set your sights on a data science internship, rather than pure deep learning job. Do well at the internship, convert the internship into a job, then actively try to incorporate ML into your work. I suspect after a few years of this, employers will be more open to considering you for ML/DL positions.
1) Degree - Depending on your finances, it might be worth checking out GaTech's online masters in CS (with ML specialization). https://www.udacity.com/georgia-tech. I'm sure there are other programs like this. I don't think Udacity/Coursera certificates are sufficient (yet) to convince employers by themselves.
2) Work experience - Instead of aiming directly for deep learning jobs (which are very difficult to get as you mention), I would aim for jobs in Data Science, Data Engineering, or even Software Engineer. These roles are more plentiful, and if you're a good employee, you might have the opportunity to move into a more ML/DL centric role after awhile. This is what I did at Amazon, moving from Business Analyst --> Data Engineer --> Software Engineer.
3) Location - I understand if it's not feasible for you, but it's an enormous advantage to live in a large city with many tech jobs. For example, earlier this year I got in an Uber with the head of data science at a startup downtown. I told her what I was working on and at the end of the ride she offered me an internship. Every week I meet new people who are pitching me projects or introducing me to new people. It's a sad but true fact that where you live still matters in 2017. Hopefully not much longer though!
4) Networking - Have you crawled LinkedIn for people from your University/hometown working in startups/tech/etc? Have you cold-emailed people you admire who work in ML for career advice?
5) Skills - How's your python? Are you comfortable with Numpy/Scikit-learn/Pandas and one or more of Pytorch/Keras/Tensorflow? Python is dominating deep learning today and I suspect employers will be looking for experience with python-based tools. Also how's your SQL? I'd guess 90% of companies today still store their data in SQL tables. At Amazon as a business analyst and data engineer I spent about 30% of my time writing SQL queries and getting the data back and forth.
If all else fails, you can just trade crypto with high leverage and take your chances
PS: Your portfolio and blog posts look great. I would consider cross-posting your blog posts on medium.