Numerical analysis software for Linux | Data mining and machine learning software | Free statistical software | Numerical libraries | Numerical linear algebra
Julia is a high-level, dynamic programming language. Its features are well suited for numerical analysis and computational science. Distinctive aspects of Julia's design include a type system with parametric polymorphism in a dynamic programming language; with multiple dispatch as its core programming paradigm. Julia supports concurrent, (composable) parallel and distributed computing (with or without using MPI or the built-in corresponding to "OpenMP-style" threads), and direct calling of C and Fortran libraries without glue code. Julia uses a just-in-time (JIT) compiler that is referred to as "just-ahead-of-time" (JAOT) in the Julia community, as Julia compiles all code (by default) to machine code before running it. Julia is garbage-collected, uses eager evaluation, and includes efficient libraries for floating-point calculations, linear algebra, random number generation, and regular expression matching. Many libraries are available, including some (e.g., for fast Fourier transforms) that were previously bundled with Julia and are now separate. Several development tools support coding in Julia, such as integrated development environments (e.g. for Microsoft's Visual Studio Code, an extension is available providing debugging and linting support); with integrated tools, e.g. a profiler (and flame graph support available for the built-in one), debugger, and the Rebugger.jl package "supports repeated-execution debugging" and more. Julia works with other languages, calling C has special support, and with use of extra packages, e.g. for working with Python, R, Rust, C++, SQL and to work with or even to compile to JavaScript. Julia can be compiled to binary executables using a package for it supporting all Julia features. Small binary executables can also be made using a different package but then the Julia runtime isn't included in the executable, e.g. down to 9 KB (then without e.g. the garbage collector since it's part of Julia's runtime, i.e. with similar limited capabilities to the C language), for computers or even microcontrollers with 2 KB of RAM. By default, Julia code depends on the Julia runtime to support all Julia features, e.g. threading, but some (non-idiomatic, to smaller or larger degree) Julia code can be compiled to small executables (with limited Julia capabilities). In both cases no source code needs to be distributed. (Wikipedia).
Setting up Julia (using Juliabox.org) to import our dataset and start our data analysis.
From playlist The Julia Computer Language
Lesson 01_01 Introducing Julia
Download the notebook files as they are added at: http://www.juanklopper.com/computer-programming/ In the first part of this introductory lesson I take a quick look at Julia. Julia is a computer language for technical (mathematical) computing. It is easy to learn, with simple syntax, ye
From playlist The Julia Computer Language
Julia has a special type for single characters.
From playlist The Julia Computer Language
In this lecture I delve a little deeper into the Distributions package used in Julia.
From playlist The Julia Computer Language
Starting to take a closer look at our data using some descriptive statistics.
From playlist The Julia Computer Language
In this section I change all the coded values back to the actual values, just to clear things up when doing the analysis.
From playlist The Julia Computer Language
Lesson 04_01 Introduction to Julia functions
One of the most useful aspects of Julia is the ability to create functions and more so, in the multiple dispatch format that functions take. In this introduction we will take a look at what's coming up in this lesson.
From playlist The Julia Computer Language
With dictionaries we create both a set of elements and specify a key for each. We can reference these keys instead of the usual indices wes used in arrays.
From playlist The Julia Computer Language
Lesson 04_05 Functions with a variable number of arguments
In this section we take a look at creating functions that can take a different number of arguments each time it is called.
From playlist The Julia Computer Language
Algorithm Archive Updates! Woo 3!
Lots of discussion today about the future of the AAA. We began implenting Julia code as the default language. We'll see how this goes. -- Watch live at https://www.twitch.tv/simuleios
From playlist Algorithm-archive
Best practice from Julia: Impact through efficient research code
The challenge of bringing projects from research to real world impact is spinning out of control. Ideas need to reach clusters and super-computers for large scale data; be deployed to the cloud for real-time analysis, and be built on by other industrial and academic projects. Adhoc soluti
From playlist Turing Seminars
The journey to Julia 1.0: The "Ju" in Jupyter
The journey to Julia 1.0: The "Ju" in Jupyter Viral Shah (Julia Computing), Jane Herriman (Julia Computing), Stefan Karpinski (Julia Computing, Inc.) Julia has seen over 2M downloads since its inception and v1.0 is expected to be released soon. It is used in areas as diverse as machine le
From playlist JupyterCon in New York 2018
DSI | Julia, The Power of Language by Alan Edelman
Abstract: The Julia language has become well known for its combination of performance and ease-of-use. We argue the real power of language is the ability to have impact. In this talk we will assume no or little familiarity with the Julia language, and describe why Julia is not just anoth
From playlist DSI Virtual Seminar Series
DSI | Julia, The Power of Language by Alan Edelman
Abstract: The Julia language has become well known for its combination of performance and ease-of-use. We argue the real power of language is the ability to have impact. In this talk we will assume no or little familiarity with the Julia language, and describe why Julia is not just anoth
From playlist DSI Virtual Seminar Series
Julia is a dynamic general purpose programming language popular for scientific computing and big data analytics. It is extremely fast thanks to its use of a JIT compiler and allows developers to write concise, yet powerful code. #compsci #programming #100SecondsOfCode 🔗 Resources Julia
From playlist 100 Seconds of Code
What's New in ExternalEvaluate
The ExternalEvaluate framework lets you evaluate code from a number of external languages and environments such as Python, Ruby and Java directly in your Wolfram session. This talk will focus on the latest additions, with particular attention to the recently added SQL, shell and Julia comp
From playlist Wolfram Technology Conference 2022
Working on the Fractal Animation Engine! https://www.leioslabs.com/Fae.jl/dev/ Discord: https://discord.gg/QctJhUA Github: https://github.com/leios Music: https://www.joshwoodward.com/
From playlist Streams
23. High Performance in Dynamic Languages
MIT 6.172 Performance Engineering of Software Systems, Fall 2018 Instructor: Steven Johnson View the complete course: https://ocw.mit.edu/6-172F18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63VIBQVWguXxZZi0566y7Wf Professor Steven Johnson talks about a new dynamic
From playlist MIT 6.172 Performance Engineering of Software Systems, Fall 2018
Algorithm Archive Updates! Woo!
I figure we can start posting my streams again, Algorithm Archive here: https://leios.gitbooks.io/algorithm-archive/content/ -- Watch live at https://www.twitch.tv/simuleios
From playlist Algorithm-archive
Lesson 04_02 Single expression functions
The first syntax for creating a function is the single expression function. It looks a lot like a mathematical function.
From playlist The Julia Computer Language