There's no language I'd want to teach (applied) math in more than Julia. Lean is obviously kind for anything involving a proof, but for teaching anything with linear algebra involved the syntax alone is enough of a reason to prefer Julia.
Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language
The stated goal is to introduce math concept without computer language or use being a burden. But then I took a quick glimpse at the derivative chapter, and at the top there is "using Sympy", so you don't only use Julia but also sympy. Mathematica and Matlab are the alternative in a university setting in which free licenses for students are available, with strong numerical and symbolic capabilities . I think Julia is a lovely language, but perhaps Julia + Sympy is a difficult proposal for attaining the stated goal. S.Rational(1) + S.Rational(1,3) type of things from sympy are hard to digest.
Edited: Added: I think if you love teaching calculus using Julia then the passion you transmit might compensate for the additional burden of using Julia plus SymPy.
I wouldn't call them experimental. They just suck IMO, and that's my biggest gripe with using Julia, as language alone is nice, especially with slowly improving AOT. Of course you will also get replies with completely opposing views, so there is no other way than trying it yourself.
Anyone using Julia outside university settings? I tried to get into it around 5 years ago, and it felt good, was conceptually interesting, but haven't seen any adoption or talks anywhere outside the academia
Yes - in multiple companies. It works perfectly fine in production, and is especially useful if mathematical optimisation is part of your app somewhere or you need to write any custom, performant algorithms. It's pretty flexible - you can use it for dynamic code like python scripts, you can write optimised data pipelines, it can function as pretty decent server (the async stuff is working well these days). Not sure on adoption, but it defintely works.
That's the only time I've ever even heard it mentioned outside HN. I felt a lot of excitement for Julia's automatic differentiation during the ML era, because you had programmers with weak math backgrounds suddenly needing to do basic calculus (any dev from that era has the chain rule burnt into their brain).
Then the ML community settled on Python as their language, and if performance was an issue you wrote a library in C++, and obviously that was all sucked into the event horizon of LLMs so people "code" in whatever language the LLMs had the best training data for.
Julia's interop is amazing. I've used AI to generate Julia code that calls cuBLASLt for efficient tensor FP8 computations. It achieves like 90% of maximal theoretical GPU performance withing a workflow written completely in Julia.
At a new-space startup I've worked at in the past we used it for some satellite (constellation) analysis work. Mechanical, Power, ConOps, that sort of stuff. MATLAB was too expensive and also overkill, as we didn't really need the toolboxes.
20 quadrillion ants is about 2.5 million ants per human - pretty insane to think about. I thought I had a grasp on their abundance, but I'd still probably have underestimated their numbers by an order of magnitude previously.
We still mog them on mass and volume though. 2.5 million of the average ant would only make the size of a human leg, if that.
Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language
Edited: Added: I think if you love teaching calculus using Julia then the passion you transmit might compensate for the additional burden of using Julia plus SymPy.
That's the only time I've ever even heard it mentioned outside HN. I felt a lot of excitement for Julia's automatic differentiation during the ML era, because you had programmers with weak math backgrounds suddenly needing to do basic calculus (any dev from that era has the chain rule burnt into their brain).
Then the ML community settled on Python as their language, and if performance was an issue you wrote a library in C++, and obviously that was all sucked into the event horizon of LLMs so people "code" in whatever language the LLMs had the best training data for.
As a language it’s very pluggable (both directions). I think it’s one the best languages in terms of interop out there at the moment.
ECMWF ASML Pumas JPMorgan Chase
and many other companies.
I use it routinely, but it isn't a company standard where I work.
We still mog them on mass and volume though. 2.5 million of the average ant would only make the size of a human leg, if that.