Dr. Chris Rackauckas (@chrisrackauckas@fosstodon.org) writes:

#julialang GPU-based ODE solvers which are 20x-100x faster than those in #jax and #pytorch? Check out the paper on how #sciml DiffEqGPU.jl works. Instead of relying on high level array intrinsics that #machinelearning libraries use, it uses a direct kernel generation approach to greatly reduce the overhead.

Read Automated translation and accelerated solving of differential equations on multiple GPU platforms

Richard McElreath has made his course materials available on GitHub.

However, the course follows the 2nd edition of McElreath's book Statistical Rethinking which is not available in a free digital format.

After watching the first lecture in the Statistical Rethinking 2023 YouTube Playlist, I might go ahead and purchase the text and use this course instead of Trevor Hastie and Rob Tibshirani's An Introduction to Statistical Learning (with Applications in R or Python) course.

I also like that this resource has made an explicit attempt to provide code examples in Julia as well as the more popular Python and R.

I wasn't sure who Richard McElreath was so I did a quick search which revealed his position as Director of the Department of Human Behavior, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology in Leipzig.

Last week I was in Münster, Germany, to discuss Spatial Data Science across the @julia, @python, and #r languages (https://r-spatial.org/sdsl/). I think it is fair to say that everyone learned a lot from one another and there are now several efforts to align the ecosystems
in terms of teaching, file formats, and others. In JuliaGeo we still have to implement some geography methods. Thanks to @edzer and Yomna for organizing.

Last month I taught @julia and its geospatial stack at the @opengeohub summer school 2023 in beautiful Poznań, Poland. I had a great time together with other open-source developers. Pictured is me calling in from the train home, because I won a hackathon by using Julia 🥳.

My materials (including the notebooks for the hackathons) are at https://github.com/evetion/OGH2023 and the recording(s) are at https://www.youtube.com/watch?v=UgMdr8vE9uM&list=PLXUoTpMa%5C_9s1C8LrkslIctOU31Oi0LAQa.

Initial commit was June 11. Documentation is here.

Julia has hit the TIOBE index top 20. This is the first time in history! Born about 13 years ago, Julia is a really young programming language. So what makes Julia unique? Why does it deserve this top 20 position? Julia is especially used in the data science and mathematical computation world. But we already have got top 20 contenders in this field such as Python, R and MATLAB. So why then Julia?

I just tried my first problem on Codewars and realised: This solution feels like cheating

“We are very excited by this new strategic investment from AEI HorizonX,” said Dr. Viral Shah, Chief Executive Officer of JuliaHub. “In recent years, Julia’s superior speed and ease of use have been put to work for pharmaceutical companies such as Pfizer and United Therapeutics, and for aerospace applications at NASA, the Federal Aviation Administration and Boeing. This investment allows us to continue to develop and help deploy Julia’s capabilities across all industries.”

Hey, I thought I could spark some discussion here, so I'll just ask what I've been wondering about.

So, what is the difference between JuMP and Optim.jl? Or is it possible to use Optim's optimizers with JuMP?

Some ELI5 would be awesome.

Thanks

A quick overview of the Julia Programming language.

The 2023 Julia User & Developer Survey is now open!

Julia users and developers are encouraged to participate. The survey is available in English, Spanish, Chinese (Simplified and Traditional), Japanese and Korean.

Results will be presented at JuliaCon in July.