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A collection of Julia links and resources

I made this collection of links for my coworkers to spread the word on Julia. A lot of this is stuff I got here from other posters on reddit. I tweaked it a bit for here to remove some of the things that relate to my job.
Please let me know if there is anything I could add or remove (though keep in mind that most people this was originally aimed at don't necessarily know much beyond Excel, VBA and SQL). If there is some tutorial that I'm missing, didn't give proper credit to someone, broken links, or any other kind of feedback, please do let me know!
ChrisRackauckas, you feature prominently here, please tell me if you're not comfortable with how I've phrased things or what I've included.


A handful of videos I've seen and liked that are available on YouTube from different JuliaCons.
  • Learnings from scaling Julia up to process petabytes in Production: Jacob Quinn, engineer from Domo talking about using Julia to do statistical analysis on huge amounts of data. 13 minute video.
  • State of the Data: Jacob from above giving an update on the state of all the different packages for data handling (CSV, DataFrames, Tables.jl, etc). 11 min.
  • Julia in a multi user production capital modelling: A large insurance company using Julia for modeling in a production environment. Tim Thornham from Aviva, an actuary, talking about building two of their production models in Julia. 57 min.
  • Julia and the Next Generation Airborne Collision Avoidance System: Lincoln Labs/FAA using Julia instead of Matlab/C++ for planes not crashing into each other (seems like a non-trivial task). 32 min.
  • JuliaRobotics: Making robots walk with Julia: I'm not really into robotics, but I found this video fascinating (and I could actually follow most of it, unlike some of the other talks at JuliaCon). Super enjoyable, at least for me. 40 min.
  • How We Wrote a Textbook using Julia: another super interesting watch. They used Julia, LaTeX and other fairly new technologies to create a textbook where they could change examples, illustrations, etc. just by changing the code. Eliminated the need for a time-consuming back-and-forth with typesetters.
  • Heterogeneous Agent DSGE Models in Julia at the FRBNY: a research analyst from the New York Fed discusses how they use Julia for their economic modeling.
  • The Unreasonable Effectiveness of Multiple Dispatch: covers how multiple dispatch differs from other programming paradigms, such as OOP, and how it allows for the extensibility in Julia's ecosystem.
  • What's Bad About Julia: straight from the horse's mouth. Jeff Bezanson, one of the original co-creators, talks about some of the well-known and less well-known issues with the language. After the first several minutes, this talk took off and went beyond what I can understand, but what it conveyed to me is that these guys are listening to the community and are actively working on solving the known problems.

Articles/Blog Posts

It’s a lot of reading, but if you're interested or curious, these articles are well worth it.
In the end, we narrowed the list based on technical merits down to Swift, Rust, C++, and potentially Julia. We next excluded C++ and Rust due to usability concerns, and picked Swift over Julia because Swift has a much larger community, is syntactically closer to Python, and because we were more familiar with its internal implementation details - which allowed us to implement a prototype much faster.
It's interesting to note, however, how one of the authors of the blog post is Chris Lattner...the creator of Swift.


A handful of podcasts I've come across where Julia is being discussed. I haven't yet listened to the ones with asterisks next to them.

Tutorials/Learning Materials

There are a handful of learning materials available on Julia, including some textbooks you could buy on topics such as Data Science and Linear Algebra. As everyone knows at this point, and continues to point out, the availability of learning materials is not as great as that of more established languages like Python or C#. But there are some, and here are a handful of resources I've come across.

The Data Ecosystem

I was going to add the first post below under Reading, since it's a blog post, but given the centrality of the topic, it deserves its own section.

Case Studies

The case studies on the Julia Computing website. A lot of interesting stuff being done with Julia, I encourage you to browse through it. I linked some of these videos up above, so browse here if you'd rather read than watch. A few of the highlights:

Musings from Chris

Chris Rackauckas is powerhouse of a Julia user and a very vocal member of the community. In fact, he's one the core members of the Julia open source community that leads the team that develops the DifferentialEquations.jl library. He is also one of the main developers of the Pumas software, which is aimed at pharmacometricans. He's currently an applied mathematics instructor at MIT and is affiliated with Julia Lab.
“I used to use a smattering of C, MATLAB, Fortran, Javascript, R, Mathematica, and Python. Yes, that's a big mess. The issue was... they all had major problems which were fundamental to their setup and design. MATLAB has no pretense of having any nice structure for developing real code (it didn't have arrays of strings until MATLAB 2017a, or any data structures like stacks or priority queues, or namespacing for packages, etc.). R and Python put simple object models on the language. R actually had 3 (now I think it has 5?) incompatible object models. With both R and Python if you actually use objects then your code slows to a crawl. That puts them in a weird spot: people say Python is object-oriented but you won't actually use objects in numerical code because looping over objects is super slow, so is it really OO if you're not supposed to be using them in any real case? Philosophical conundrum.
And then there's Javascript. I tried contributing to some Javascript numerical libraries and learned why people don't even like it for web development.
I was trained in C and Fortran for HPC and MPI, so those were tools I carried around with me. MATLAB's MEX interface is complicated as all hell (take a look for yourself if you've never seen it) so I never really interfaced them all that much with MATLAB, but using them on their own is a usability joke (outputting files to plot later! :) ). With Python+R I built a multilanguage monstrosity but wasn't happy with it. Needless to say, this setup could get stuff done but only was pieced together by duct tape and I knew exactly what the unfixable problems were so I wasn't happy with it.
So in graduate school I wrote 3 attempts at a stochastic partial differential equation solver library in MATLAB, basically trying again and again to get something decent by building a DSL from string parsing and then using a bunch of options to dig down into GPU-parallelized kernels. Stefan Karpinski says that in any sufficiently large library there's an implementation of multiple dispatch, and it definitely rings true here. When I finally got some adaptive stochastic differential equation solvers working, the big hold up was that the lack of efficient data structures (stacks and priority queues) along with the fact that it had to be written as quick loops means that my benchmarks were only okay.
So I took the dive to try Julia, and when I re-wrote what I had been working on it became DifferentialEquations.jl. Needless to say, that re-write worked out quite well so I have uninstalled everything else and only use Julia now.
While Julia isn't without issues, it is without unsolvable issues. That's what I really like about it from a developer standpoint. MATLAB is a blackbox that you cannot change. R and Python will never have fast objects (by design they cannot compile to anything efficient given their mutability of field structure among other things). Numba and Cython are fine if you work with only Float64 codes, but that's the same issue of throwing away the whole object model (in recent years they got a way to write simple objects only compatible in these frameworks, but you can't simply re-write the standard library yourself to get some objects because they aren't compatible with the operations of Python objects... yay?). Without multiple dispatch its hard to get any kind of generic programming going in Numba/Cython or efficiently write codes which need heavy specialization (numerical codes). I don't like the local optima that R or Python puts you in where it gives you unsolvable issues and alters your code for performance.
But Julia is you and me. The Base library is Julia code. If you don't like how it's performing, do u/ and see what it's doing. I've modified many many Julia packages to get what I need since it's a simple flip to go from user to developer. And the core Julia issue, the next steps beyond the simple JIT model, already have solutions. There are ways to statically compile Julia code, and there is a Julia interpreter that has been written so that not all code has to be compiled. These haven't been incorporated well into Julia, but that's just a tooling issue. Julia still has issues because it is young, but those issues actually have real solutions, and I can contribute to them directly using Julia code!
And I'll leave you with this. Python's manual literally says
It is quite easy to add new built-in modules to Python, if you know how to program in C
Here's the link: https://docs.python.org/3/extending/extending.html . Yes, Python is super easy if you know C guys. There's the whole page showing you how to make pointers to Python objects, just the way you've always wanted to write your numerical codes if you wanted to loop fast... uninstalled.”


The Julia community are physically scattered all over the world, although Julia Lab is centered at MIT. Here is where they hang out online:
  • Julia Discourse: this is where most of the community is, including the core developers. One of the better places to ask questions.
  • Julia Slack: you need a registration/invite for this. Supposedly the quickest place to get an answer.
  • www.reddit.com/Julia/: pretty self-explanatory. Go here if you're embarrassed to ask questions on the big dog forums (though some of the big dogs are on reddit as well). This forum is not properly moderated so occasionally low quality content pops up, unlike Discourse and Slack.
  • And of course, the whole project is hosted on Github, where issues get reported and discussed.
  • Blog on the Julia Computing site: this contains announcements related to their products/services and other goings-on in Julia world.
  • Blog on the Julia Lang site: this is the more interesting of the two official blogs, it concerns developments in the language and its ecosystem.

Notable Libraries

Here are some of the notable libraries that have been developed in the Julia ecosystem. A few of these are considered state of the art/cutting edge (see Chris's musings). Although there are not nearly as many libraries as there are for R or Python, there are still quite a bit. Moreover, quantity does not equal quality. So far Julia has a small quantity of high-quality libraries written by very dedicated members of the community.
  • Flux: one of the main machine learning libraries. Haven't used it, but I've read that it's a good way for a beginner to explore machine learning. The word flux is a not-so-subtle reference to Google's TensorFlow library for machine learning. The 1.x series of TensorFlow is notoriously difficult to use, which is why the Flux authors captioned their library with "Relax! Flux is the ML library that doesn't make you tensor". However, the most direct analog to Flux would probably be PyTorch. Both TensorFlow and PyTorch are Python-based tools.
  • JuMP: a domain-specific modeling language for mathematical optimization embedded in Julia. It currently supports a number of open-source and commercial solvers ... for a variety of problem classes, including linear programming, (mixed) integer programming, second-order conic programming, semidefinite programming, and nonlinear programming. JuMP makes it easy to specify and solve optimization problems without expert knowledge....
  • DifferentialEquations: <- that's the project homepage, and here is the README on Github. Supposedly one of the most comprehensive suites of diffeq solvers available anywhere.
  • JuliaStats: a meta-library for all sorts of probability and statistics functionality, including Distributions, a package for probability distributions, and GLM, a package for linear and generalized linear models in Julia.
  • CSV: A fast, flexible delimited file readewriter for Julia.
  • DataFrames: Tool for working with tabular data in Julia. Supports missing values. Julia's equivalent to Python's Pandas.
  • Plots: a meta-library of plotting libraries, including ones popular for Python and R use.
  • GadFly: a plotting and data visualization system written in Julia. Based on the idea of ggplot2.
  • StatsPlots: a plotting library for use with the JuliaStats packages.
  • Makie: more visualizations!

Julia Survey

Here is a link to the survey they recently conducted, which includes the slides they presented at JuliaCon 2019.
submitted by EarthGoddessDude to Julia

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