Mocha.jl

There is https://github.com/pluskid/Mocha.jl but it seems more focused on vision tasks at least for now.

1. Speed in iterative algorithms. 2. (Almost) painless concurrency. 3. Symbolic programming right in the language (think of SymPy built in the language). 4. Closeness to Matlab (which makes it easy to translate existing code). In general, people tend to port to Julia algorithms that were originally written in Matlab or C++ (with the conciseness of first and speed of the second). For example, I made several attempts to translate Matlab/C++ code of active appearance models to NumPy, but succeeded only with Julia [1]. Another example - Mocha.jl [2] - is a port of Caffe deep learning framework (written in C++ with a wrapper in Python) into a pure Julia, which it easy both to use and modify. So if you just use existing libraries, there's probably no big difference what to use. But if you actively write new libraries, at least give Julia a try.

The author mentions that one of his goals was to focus on a smaller set of functionality and make it simple and high-performance, but I've got to put a shoutout to Mocha.jl here [1]. It is essentially Julia's answer to the Caffe deep learning framework (which is linked in the article), and has pure Julia, C++, and CUDA GPU backends. Its under active development but is already pretty amazing. Bonus: it has documentation! On the contents of this blog post: I really like how the Julia type system is used here. Not only do the types help structure the code and send a signal to the user, but of course there is type-checking to catch errors. [1]: https://github.com/pluskid/Mocha.jl

I feel like people are perhaps a bit overly negative about the state of Julia's packages. Compared to other early-stage languages, I'd say our package ecosystem is vibrant and full of fantastic packages. Naturally perhaps they are more math/scientific focussed, so if you are looking for cutting-edge packages for web dev you won't find them yet (although there are packages!). See http://pkg.julialang.org/pulse.html, for example. We have over 470 packages in total that are registered, and on Julia 0.3 we have over 300 packages with tests that pass - and we run the tests in all registered packages every night. Some of my favorite packages (that I didn't make, of course :D) would include https://github.com/JuliaStats/Distributions.jl https://github.com/JuliaStats/StatsBase.jl https://github.com/pluskid/Mocha.jl (deep learning) https://github.