[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