Julia resources

Why we created Julia

by Jeff Bezanson, Stefan Karpinski, Viral Shah, Alan Edelman

Julia tutorials

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Courses that use Julia

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  • Introduction to Numerical Analysis, MIT 18.330, Spring 2017

    "This course is an exploration of the art and science of extracting numbers from mathematical expressions. The material we will cover may be broadly divided into two units.

    Unit 1 is all about basic numerical calculus. We will discuss elementary methods for obtaining accurate numerical estimates of integrals, derivatives, and infinite sums. This unit will include discussions of extrapolation, interpolation, root-finding, optimization, and evaluation of special functions.

    This will set the stage for Unit 2 of our course, Fourier analysis and spectral methods. The overarching theme here is that we can often revolutionize the speed and accuracy of a calculation by approximating a function as an expansion in some convenient set of expansion functions -- often a set of orthogonal functions. Our discussion of orthogonal-function expansions will begin, as must any, with the granddaddy of them all: the Fourier series and its immediate descendants (the Fourier transform, Parseval's and related theorems, the FFT, etc.). Then we will broaden the setting to consider more general classes of functions and more general spectral methods: Gaussian quadrature, Chebyshev polynomials, ... and more.

    The examples are drawn from engineering and the sciences, including binding energies of solids, coding and modulation schemes for efficient use of the wireless communications spectrum, spherical Bessel functions for electromagnetic scattering and thermal engineering, and Ewald summation."

  • Introduction to Numerical Methods, MIT 18.335, Spring 2015

    Advanced introduction to numerical linear algebra and related numerical methods. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating-point standard, sparse and structured matrices, and linear algebra software. Other topics may include memory hierarchies and the impact of caches on algorithms, nonlinear optimization, numerical integration, FFTs, and sensitivity analysis. Problem sets will involve use of Julia, a Matlab-like environment (little or no prior experience required).

  • Advanced Topics in Scientific Computing with Julia, Stanford CME257, Fall 2015

    The course is intended to introduce students who are already somewhat familiar with scientific computing to what Julia has to offer.

    1. Programming in Julia
      1. Basics / getting started
      2. Object Oriented Features, type system
    2. The Julia Ecosystem
      1. Common scientific computing packages, and how to use them - graphics, math packages, graph theory, optimization, etc.
      2. Intro to GitHub
    3. Practical Julia
      1. How to write your own package/module, more GitHub
      2. Interfacing with C/Fortran shared object libraries, PyCall
    4. Julia in the Wild
      1. Debugging, performance, etc.
      2. Special topics - parallelism, linear algebra implementations, blas calls, etc., the future of Julia

Julia official documentation

Julia unofficial anime character

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Hello, I am Julia-tan #JuliaLang (unofficial) anime character!

You can use my picture in Non-Commercial use by following CC BY-NC-SA license Creative Commons License.

Julia-tan is partly licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

If you would like to use my picture in Commercial use (such as using a picture in cover-page of the books), please consult to Takeshi KIMURA (twitter: @kimrin).

Saved from on Oct 10, 2015