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Acorn.jl is a small text editor written purely in julia. Note: This project was written to learn more about and demonstrate julia as a general purpose language, it was not originally intended to be a practical solution to editing text within the REPL (considering one can just type ;vim for a feature complete text editor in the REPL).

https://github.com/nick-paul/Acorn.jlTags | julia julia-language text-editor terminal |

Implementation | Julia |

License | MIT |

Platform |

Julia.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-listSeveral MIT courses involving numerical computation, including 18.06, 18.303, 18.330, 18.335/6.337, 18.337/6.338, and 18.338, are beginning to use Julia, a fairly new language for technical computing. This page is intended to supplement the Julia documentation with some simple tutorials on installing and using Julia targeted at MIT students. See also our Julia cheatsheet listing a few basic commands, as well as the Learn Julia in Y minutes tutorial page. In particular, we will be using Julia in the IJulia browser-based enviroment, which leverages your web browser and IPython to provide a rich environment combining code, graphics, formatted text, and even equations, with sophisticated plots via Matplotlib.

The 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-computingJulia 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-learningIJulia is a Julia-language backend combined with the Jupyter interactive environment (also used by IPython). This combination allows you to interact with the Julia language using Jupyter/IPython's powerful graphical notebook, which combines code, formatted text, math, and multimedia in a single document. to install IJulia.

This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them. Within Julia, just use the package manager to run Pkg.add("PyCall") to install the files. Julia 0.5 or later is required.

This is the GitHub repository for the Julia programming language project's main website, julialang.org. The repository for the source code of the language itself can be found at github.com/JuliaLang/julia. The Julia website is generated using GitHub pages and Jekyll, as explained here.

This is the GitHub repository for the Julia programming language project's main website, julialang.org. The repository for the source code of the language itself can be found at github.com/JuliaLang/julia. The Julia website is generated using GitHub pages and Jekyll, as explained here.

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. This computation is automatically distributed across all available compute nodes, and the result, reduced by summation (+), is returned at the calling node.

language programming-language statistical-language statistics technical-computingPowerful convenience for Julia visualizations and data analysis

julia plotting visualization julia-languageJuMP 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-languageNews: Turing.jl is now Julia 1.0 compatible now! Be aware that some things still might fail. Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include Hong Ge, Adam Scibior, Matej Balog, Zoubin Ghahramani, Kai Xu, Emma Smith, Emile Mathieu, Martin Trapp. You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.

machine-learning probabilistic-programming mcmc-sampler julia-language artificial-intelligence bayesian-inferenceCoCalc 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 juliaThis is a draft of the book Text Mining with R: A Tidy Approach, by Julia Silge and David Robinson. Please note that this work is being written under a Contributor Code of Conduct and released under a CC-BY-NC-SA license. By participating in this project (for example, by submitting a pull request with suggestions or edits) you agree to abide by its terms.

book text-mining tidyverse bookdown rKnet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow. GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data. Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency. Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels. See Under the hood for more details.

Until 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-computingJulia 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 latex
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