Do you think this would be appropriate for a command line tool that hits various apis as the function calls? Ex: "what's the weather in SF tomorrow?" Or "daily price change of apple, Tesla stock for past week"? (Let's assume I have documented the apis thoroughly somewhere that the model has access to or fine tuned it on this data)
Does anyone know what the state of the art industry solvers do for these problems? I had dabbled a bit in ml approaches to combinatorial optimization with great interest a few years back, but I don't think any of these rl based methods ended up being used in production.
When people learn about distributed systems outside of work, how do they actually get hands on experience with it (assuming they don't go spinning up a bunch of machines on aws/gcp/azure/etc)? I find it easiest to learn by doing, writing simple proof of concepts but that seems a bit harder to do in this area than others? What is the hello world/mnist of messing around with distributed systems?
Learn linear algebra properly. Like many folks on HN, I really enjoyed Axler's Linear Algebra Done Right, but ymmv. I got a ton out of going through each of the proofs + practice problems and really taking the time to work through the solutions. It wasn't enough for me to just read the text.
There's a linear algebra lurking everywhere in the realm of applied math (some people like to joke that machine learning is really just linear algebra) so it really is worth your time to have a firm understanding of it.