Context In a few days, I’m going to go try to sell (as in persuade, not monetarily) a short Julia course to some first year MSc AI students. The majority of these students come from a comparatively stronger mathematical background. So far, they have been taught PyTorch and will use it to submit to …

Cuda.jl : the type system allows for writing a generic code running fast on both CPU and GPU. ParallelStencil.jl demonstrate this too.

I can’t go past Unitful and how you can propagate units with almost no friction, then giving an example of defining your own unit (maybe a whimsical one) and using it to immediately do a calculation.

Forward mode automatic differentiation: https://www.wias-berlin.de/people/fuhrmann/AdSciComp-WS2223/week3/#automatic_differentiation_and_nonlinear_system_solution Got the idea from this thread: https://julialang.zulipchat.com/#narrow/stream/225542-helpdesk/topic/Comparing.20julia.20and.20numpy/ne…

[image] Datseris: The DifferentialEquations.jl and Measurements.jl example from Stefan’s talk [image] jd-foster: I can’t go past Unitful and how you can propagate units with almost no friction Yes! These are both fantastic, canonical examples that can be demonstrated in a TikTok, let a…

[image] jacobusmmsmit: Forward mode automatic differentiation Beautiful. These kids won’t know what hit them! I think a great title to my slides would be “Can your Python do this?”. Huh, my reaction to this was that python ought to have an answer to this, but… not really, apparently. (of c…

Even “simpler” example, using Measurements.jl: using Measurements x = 2.0 ± 0.5 # \pm<TAB> y = 5.0 ± 1.0 z = 7.5 ± 1.5 typeof(x) # Measurement{Float64} (x + y) * z # 52.0 ± 13.0 (x + y) / z # 0.93 ± 0.24 exp(x) # 7.4 ± 3.7 and composing it with Unitful: …

Off topic but quick comment of appreciation to Rafael’s seemingly wiki-like ability to provide an exact link to obscure pieces of the Julia manual and old Discourse posts :slight_smile: I’ve never seen MonteCarloMeasurements.jl before! Looks great.

https://indico.cern.ch/event/1074269/contributions/4539601/attachments/2317518/3945412/why-julia slides.pdf#page=22 see page 22, writing a GPU compatible DenseLayer in 4 lines

One of the things that may well cause a strong impression in math-oriented people is the possibility of optimizing parameters of a simulation by automatically differentiating an objective function which is dependent on the simulation itself. One example of this, a little bit artificial to make it s…

Seeking short examples to "sell" Julia's type system

Community Teaching & Outreach

rafael.guerra January 25, 2023, 1:42pm 8

This simple example of Plots + MonteCarloMeasurements by @jonniedie is a killer!

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Seeking short examples to "sell" Julia's type system

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