The following pages and posts are tagged with
| Title | Type | Excerpt |
|---|---|---|
| Automatic differentiation | Page | * Working out many functions and partial derivatives can be time-consuming and error-prone. * Function, even Julia functions, not just mathematical ones, are made up of elementary differentiable operations, such +, -, sin, cos, etc. So a computer program should be able to differentiate those functions. * Automatic Differentiation, AD,... |
| Basics | Page | This tutorial is for people who have a basic exposure to programming. It is a compendium of several tutorials[^1] and [the official documentation](https://docs.julialang.org/en/v1.1/){:target="_blank"}. ## The REPL When you type `julia` in the terminal or command prompt, or just click on the Julia icon, you enter the REPL (Read/Evaluate/Print/Loop). This is... |
| Installing Julia | Page | ## How to install Julia from pre-built binaries You download pre-built binaries of the latest version from [this page](https://julialang.org/downloads/){:target="_blank"}. There are binaries for Windows, Linux, and Mac. The current stable release is 1.6.3. After downloading the binaries for your operating system, follow the procedures below to install Julia. More detailed... |
| About Julia | Page | ## About Julia Julia is a free language that is easy to use and is specifically suitable for machine learning, data science, and scientific computing. It was first public in 2012 and its first stable version (v1.0) was released in 2018. The language has been inspired by many of good... |
| Parallel computing | Page | Julia comes ready for parallelizing your code. It does not need any external libraries for that. ## SIMD Single Instruction, Multiple Data (SIMD) is method of parallelization where a single operation can be applied to multiple variables, simultaneously. SIMD is useful when the following conditions are true: * Each iteration of... |