(Former AI researcher + current technical founder here) I assume you’re talking about the latest advances and not just regression and PAC learning fundamentals

(Former AI researcher + current technical founder here) I assume you’re talking about the latest advances and not just regression and PAC learning fundamentals. I don’t recommend following a linear path - there’s too many rabbit holes. Do 2 things - a course and a small course project. Keep it time bound and aim to finish no matter what. Do not dabble outside of this for a few weeks :) Then find an interesting area of research, find their github and run that code. Find a way to improve it and/or use it in an app Some ideas. - do the fast.ai course (https://www.fast.ai/ https://www.fast.ai/) - read karpathy’s blog posts about how transformers/llms work (https://lilianweng.github.io/posts/2023-01-27-the-transformer-family-v2/ https://lilianweng.github.io/posts/2023-01-27-the-transforme... for an update) - stanford cs231n on vision basics(https://cs231n.github.io/ https://cs231n.github.io/) - cs234 language models (https://stanford-cs324.github.io/winter2022/ https://stanford-cs324.github.io/winter2022/) Now, find a project you’d like to do. eg: https://dangeng.github.io/visual_anagrams/ https://dangeng.github.io/visual_anagrams/ or any of the ones that are posted to hn every day. (posted on phone in transit, excuse typos/formatting)