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Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.

julia julia-language programming-language scientific-computing high-performance-computing numerical-computation machine-learningHydrogen is an interactive coding environment that supports Python, R, JavaScript and other Jupyter kernels. Checkout our Documentation and Medium blog post to see what you can do with Hydrogen.

data-science jupyter ipython repl hydrogen atom jupyter-kernels nteract execute run julia torch ijulia irkernel itorch plot imageJulia.jl aggregates and curates decibans of knowledge resources for programming in Julia, an all-purpose programming language that addresses the needs of high-performance numerical analysis and computational science. For Base packages, check if the package you seek is listed in the built-in package manager on github, or check METADATA for registered Julia packages, then use the built-in package manager to install it after checking the requirements for respective versions. Pkg3.jl is an alpha next-generation package manager for Julia that creates a Manifest.toml file that records the exact versions of each dependency and their transitive dependencies.

julia julialang awesome-listRun scripts based on file name, a selection of code, or by line number. NOTE: Some grammars may require you to install a custom language package.

script runner bash behat-feature coffeescript coffeescript-(literate) cucumber-(gherkin) elixir f# fsharp f* fstar haskell julia latex mongodb newlisp powershell rspec ruby-on-rails run code lammpsPowerful convenience for Julia visualizations and data analysis

julia plotting visualization julia-languageThe well-optimized DifferentialEquations solvers benchmark as the some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the "standard" C/Fortran methods, and include algorithms optimized for high-precision and HPC applications. At the same time, it wraps the classic C/Fortran methods, making it easy to switch over to them whenever necessary. It integrates with the Julia package sphere, for example using Juno's progress meter, automatic plotting, built-in interpolations, and wraps other differential equation solvers so that many different methods for solving the equations can be accessed by simply switching a keyword argument. It utilizes Julia's generality to be able to solve problems specified with arbitrary number types (types with units like Unitful, and arbitrary precision numbers like BigFloats and ArbFloats), arbitrary sized arrays (ODEs on matrices), and more. This gives a powerful mixture of speed and productivity features to help you solve and analyze your differential equations faster. For information on using the package, see the stable documentation. Use the latest documentation for the version of the documentation which contains the un-released features.

differential-equations differentialequations julia ode sde pde dae stochastic dde spde delay monte-carlo-simulation stochastic-processes stochastic-differential-equations delay-differential-equations partial-differential-equations differential-algebraic-equations simulation numerical-integration dynamical-systemsTools for working with tabular data in Julia. Maintenance: DataFrames is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators.

julia data-frame datasetsJuMP is a domain-specific modeling language for mathematical optimization embedded in Julia. It currently supports a number of open-source and commercial solvers (Artelys Knitro, BARON, Bonmin, Cbc, Clp, Couenne, CPLEX, ECOS, FICO Xpress, GLPK, Gurobi, Ipopt, MOSEK, NLopt, SCS) 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, yet at the same time allows experts to implement advanced algorithmic techniques such as exploiting efficient hot-starts in linear programming or using callbacks to interact with branch-and-bound solvers. JuMP is also fast - benchmarking has shown that it can create problems at similar speeds to special-purpose commercial tools such as AMPL while maintaining the expressiveness of a generic high-level programming language. JuMP can be easily embedded in complex work flows including simulations and web servers.

optimization julia modeling-languageCoCalc offers collaborative calculation in the cloud. This includes working with the full (scientific) Python stack, SageMath, Julia, R, Octave, and more. It also offers capabilities to author documents in LaTeX, R/knitr or Markdown, storing and organizing files, a web-based Linux Terminal, communication tools like a chat, course management and more. You can easily use CoCalc on your own computer for free by running a Docker image.

sagemath cloud jupyter-notebook jupyter latex r octave markdown mathjax terminal coffeescript postgresql nodejs mathematics gap pari juliaThe Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general purpose language and you'd be hard-pressed to find tasks it's completely unsuitable for (although that does not mean it's the best or easiest choice for any of them). Julia was developed with the occasional reference to R, and with an avowed intent to improve upon R's clunkiness. R is a great language, but relatively slow, to the point that most people use it to rapid prototype, then implement the algorithm for production in Python or Java. Julia seeks to be as approachable as R but without the speed penalty.

julia learning-julia language learning learning-by-doing julia-language julialang data-science statistics technical-computing hpc scientific-computingNote: The functionalities related to conjugate priors have been moved to the ConjugatePriors package.

julia probability-distribution statisticsUnivariate and multivariate optimization in Julia. Optim.jl is part of the JuliaNLSolvers family.

optim julia optimizationA wrapper around TensorFlow, a popular open source machine learning framework from Google. See a list of advantages over the Python API.

wrapper gpu julia tensorflow machine-learningUntil an issue with one of our dependencies is resolved, LightGraphs will not work with any Julia 0.7 or 1.0 version that has been built from source on OSX or other systems with a compiler more modern than GCC7. If you use LightGraphs with Julia 0.7 or 1.0, please download a Julia binary. LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type.

julia graph lightgraphs graph-theory graph-generation graph-analytics graph-algorithmsParallelAccelerator is a Julia package for speeding up compute-intensive Julia programs. In particular, Julia code that makes heavy use of high-level array operations is a good candidate for speeding up with ParallelAccelerator. With the @acc macro that ParallelAccelerator provides, users may specify parts of a program to accelerate. ParallelAccelerator compiles these parts of the program to fast native code. It automatically eliminates overheads such as array bounds checking when it is safe to do so. It also parallelizes and vectorizes many data-parallel operations.

julia parallel-computingAn image processing library for Julia. Full documentation is found at JuliaImages.

julia image-processingJulia support for Vim. The full documentation is available from Vim: after installation, you just need to type :help julia-vim.

julia vim vim-plugin unicode latexThis workshop focuses on building a production scale machine learning pipeline with Julia, Docker, Kubernetes, and Pachyderm. In particular, this pipeline trains and utilizes a model that predicts the species of iris flowers, based on measurements of those flowers.Finally, we provide some Resources for you for further exploration.

julia-language julia data-science machine-learning docker kubernetes pipelines
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