[duckworthd]

What's worked well for me: Find a way to put what AI/ML on your critical path. Think of it like learning a new language: classes, lessons, and watching TV helps, but nothing works like full-on immersion. In the context of AI/ML, that means find a way to turn AI/ML into your full-time job or school. It's not easy! But if you do, you'll see endless returns.

If you don't have a solid enough footing to get a job in the field yet, the next best thing in my opinion: find a passion project and keep cooking up new ways to tackle it. On the way to solving your problem, you'll undoubtedly begin absorbing the tools of the trade.

Lastly, consider going back to school (a Bachelor's or Master's, perhaps?). It'll take far more than 1 hour/day, but I promise you, you'll see results far faster and far more concretely than any other learning strategy.

Good luck!

Context: I've been a Researcher/Engineer at Google DeepMind (formerly Google Brain) for the last ~7 years. I studied AI/ML in my BS and MS, but burnt out of a PhD before publishing my first paper. Now I do AI/ML research as a day job.

[atomicnature]

Yes, I was leaning more towards the "personal project" idea as well, something around document understanding. I subscribe to the "learning by doing/immersion" philosophy as well (upto a large extent).

The problem with projects is one's understanding tends to go more and more specialised, and collaborating/connecting with other ML engineers requires a broader knowledge base sometimes.

Also, for giving advice and useful inputs to others (on their projects), I feel a balanced knowledge base is useful.

Hence the question.

[markha]

Greg Brockman's blog[1] has few links on how he picked up ML. Another link at [2] describes the path Michal(blog's author) followed (though it's aligned to "how i got into ..."). Both these blogs walk through how they were able to get into the ML bits of things. They have bunch of links (ex: [3]).

I think it'll help if you can get a job at a company who's main focus is ML, you'll talk to folks who are doing research or solving problems using ML, you'll learn. If not, i hope these links help as folks there (people way smarter than me, a swe) had similar question and documented the steps they took to reduce the gaps in their understanding.

[1] - https://blog.gregbrockman.com/how-i-became-a-machine-learnin... [2] - https://agentydragon.com/posts/2023-01-11-how-i-got-to-opena... [3] - https://github.com/jacobhilton/deep_learning_curriculum

[atomicnature]

Great resources, especially Brockman's blog makes the experiences so much acceptable, knowing that even the top people had to struggle to get going in ML