[big update 🥳] I'll be joining @Google@DeepMind later this year as a Research Engineer!!! 🤖😅❤
It feels surreal, I don't even know where to start. 1/
I didn't come from an "Ivy League" university. Not because I couldn't get in - but because when you're 19, from a less developed country, and you didn't have tech-savvy people around you growing up - you are not even aware that @Stanford/@MIT are a thing. 2/
I can deeply empathize with many because of that. Not all of us were lucky enough to get exposed to tech when we were 9. I "heard" about programming when I was 19! We had to compensate by working harder than everyone else. 3/
I don't have any official ML education! Yup, no PhD in ML. I even dropped out from my masters. In my case it was all by design I knew what I want. Everything I've learned was because I was ready to put in enormous effort to create my own curriculum and learn stuff on my own. 4/
Million research papers later.
65 YouTube videos on ML later.
8 open-source deep learning projects later.
Many blogs later.
Almost 3 years of work at Microsoft later.
And here I am. 5/
I've covered so much about @DeepMind until this point. Since I started learning about ML (back in 2018 - fairly recent, right? 😅) I knew I want to work at DeepMind - and I'm finally here!!! 6/
I want to thank Cameron Anderson and Saima Hussain for walking me through this process and being supportive! As well as my family and friends! ❤
I also want to thank @PetarV_93 and @relja_work. You guys are my heroes. I learned so much from you. Keep rocking! 7/
I know one thing. This is just a start. If you want to follow along my journey follow me on my:
Also join The AI Epiphany Discord community, I am building an amazing ML community there: discord.gg/peBrCpheKE
Among other things I'll be sharing tips on how you can land your dream ML job as well. 9/
Last but definitely not least I want to dedicate this success to my buddy Srdjan Gavrilovic who passed away 2 years ago in a tragic accident. We love you and we miss you man.
Just don't give up.
Hang in there.
You'll get there! ❤
I've tried to give you all of the tips and tricks I could think of both for more productive learning in general and stuff specific to the RL field.
The structure of the blog: 1) Intro 2) RL 101 (getting you exposed to the terminology) 3) Cool things about RL (awesome RL apps!) 4) RL is not just roses 5) Getting started with RL 6) Going deeper - reading papers 7) Implementing an RL project from scratch 8) Related subfields
1) This time the project is still not completely ready**. I'm yet to achieve the published results - so I encourage you to contribute!
Many of you have been asking me whether you can work on a project with me and I'll finally start doing it that way - from now onwards. ❤
2) This repo has the ambition to grow and become the go-to resource for learning RL. So collaborators are definitely welcome as I won't always have the time myself.
** main reasons are:
a) I was very busy over the last 2 weeks
b) It currently takes ~5 days to fully train DQN