Learn JULIA with Real Code Examples

Julia is designed for high-performance technical and numerical computing.

It uses JIT (Just-In-Time) compilation via LLVM for C-like speed.

Multiple dispatch allows highly expressive and extensible designs.

Ideal for data science, ML, simulations, optimization, and scientific research.

Multiple dispatch as core semantic model

Type system with optional types

First-class performance tooling

Metaprogramming & macros

GPU support via CUDA.jl & AMDGPU.jl

Scientific libraries built into Base

Types & multiple dispatch

Arrays & broadcasting

Macros & metaprogramming

Modules & packages

Parallelism & concurrency

Project.toml

Manifest.toml

src/ module files

test/ folder

scripts/ utilities & experiments

Start REPL

Activate environment

Add packages via Pkg

Write modules & functions

Run scripts or use Pluto notebooks

Package code for reuse

Beginner: basic math, arrays, plotting

Intermediate: types, modules, optimization

Advanced: multiple dispatch design, macros

Expert: GPU kernels, AD, scientific engines

Faster than Python for number-heavy workloads

More modern than R for scientific workflows

Less general-purpose than Java/C#

More efficient than MATLAB for HPC

Better multiple dispatch than CLOS

2009 – Initial development

2012 – Public announcement

2018 – Julia 1.0 release

2019–2025 – Expanding HPC & ML ecosystem

Multiple Dispatch: method selection based on argument types

Type Stability: return type predictable from input types

Broadcasting: element-wise operations with dot syntax

REPL: interactive Julia shell

Macro: code-generation tool starting with @