TL;DR Long due thank you note to Jeremy and this community, fastai changed a lot in my life, AGAIN!
I have been waiting to write this for 3 months but due to a hectic work schedule and other commitments, I couldn’t find the time and I felt bad every passing day, so here it goes.
I started my journey with fastai almost 2 years ago and easily got a job at Amazon (linked above), that was the first turning point in my life and first acceptance of my skills as a deep learning researcher without any formal computer science or ML background.
I found parts of my work toxic to my research aptitude and health in general and decided to leave within 10 months and again sit back and re-visit fastai courses and notebooks in great detail. This was indeed the most productive time, I found new things every time I re-watched a lesson (I suggest that, if you like watching something while eating). This boosted my confidence and I applied for Google AI Residency again, my 3rd attempt, and I cleared the pre-screening and went on for 2 rounds. I’m happy to talk about it in details but overall I can say whatever we learn with fastai is sufficient for anyone to apply and make it, although I didn’t make it but I can say with good confidence that the reason was not my deep learning knowledge.
Takeaway: Don’t be afraid of AI labs, always try!
While applying for residency, I also applied to more than 500 jobs outside India and fortunately got 3 replies back, 2 from London and 1 from Canada and I received offers and visa sponsorship from all three. Currently I am working in London as a computer vision researcher and between changing jobs and countries since October 2018, I somehow managed to complete both parts of 2019 course. I must say that the second part of 2019 is as eye opener for me, ‘Foundations’ idea really worked for me and it sparked more confidence and enthusiasm.
Takeaway: Apply for ambitious roles and countries, knowing odds are roughly 0.5%. Also, be regular with something which makes you feel positive and helps in learning, basically fastai 
My current job allows me to implement research papers and produces results, I recently implemented an improved version of hierarchical CNN (HD-CNN), using all my learning from part 1 and part 2, this was only possible because we are all taught how to read the greek and write code in this course. It’s my 2nd productive(making a few million for the company) implementation and I plan to implement more whenever I find time.
Takeaway: Research papers are weekend friends, they don’t sound cool but are very satisfying once you make a habit
A month ago (after 4 years of constant struggle and learning), I received my first invite as a DL speaker for a conference in London, I’ll be talking about ways of using computer vision to boost business productivity. At the same time, I also received an offer to write a book on deep learning and pytorch from Packt (can’t talk a lot about it for now but I promise to find free e-copies for this forum when it’s finished, and of course if you are interested). Foundations also encouraged me to start learning swift so I spend 2 hours every week learning swift and trying to create my own version of fastai library for a specific use case, maybe it will become something someday 
Takeaway: Don’t give up easily, it takes time to reap benefits of what you sow
My only regret is that I failed to contribute to fastai library even after so many tries, until last year I always felt inferior about my quality of code and understanding of concepts, and lately due to involvement in so many things. I hope I’ll find some time and confidence to support harebrain, once I complete with other commitments.
I would also like to mention that Sylvian, @radek and Christine were my source of inspiration throughout, I always looked up to their achievements and efforts to motivate myself.
Thanks if you read so far, I really appreciate it and I hope it helped