The Julia Programming Language Courses

Optimizing Over Trained Neural Networks with MathOptAI.jl

Discover MathOptAI.jl for embedding neural network surrogates into JuMP optimization models, with practical power flow examples and GPU acceleration techniques.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

MathOptInterface: A Comprehensive Overview

Discover Julia's MathOptInterface.jl package for mathematical optimization modeling and solver integration in this comprehensive technical overview.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

How to Hack into Documenter.jl - Extending Documentation Pipeline with Custom Hooks

Discover how to extend Documenter.jl by hooking into its pipeline stages, manipulating document structures, and creating custom blocks for different rendering backends.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Task Scheduling and Fairness in Julia

Explore task scheduling fairness in Julia, examining a one-line patch's dramatic effects, work-stealing scheduler implications, and potential alternatives for better performance.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Using Julia for Advent of Code

Discover how Julia's interactive REPL, JIT compilation, and metaprogramming capabilities make it ideal for solving Advent of Code puzzles efficiently and elegantly.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Automatic Generation of JuMP.jl Constraints from ModelingToolkit.jl Models

Discover how to seamlessly integrate ModelingToolkit.jl models into JuMP optimization frameworks, enabling global optimization of complex engineering systems with deterministic solvers.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

TIMES2JuMP - Learnings from IEA-ETSAP Feasibility Study of Migrating the TIMES Code to Julia JuMP

Discover key insights from migrating TIMES energy modeling code to Julia JuMP, exploring feasibility challenges and optimization benefits for energy system analysis.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Large Scale JuMP Models with Constraint Generators

Discover how GenOpt extends JuMP to handle billion-constraint optimization problems through parallelized constraint groups, enabling efficient cluster distribution and GPU acceleration.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

The Life and Times of SDDP.jl

Discover the 10-year evolution of SDDP.jl, a JuMP extension for sequential decision problems, exploring its real-world applications and technical rewrites leveraging Julia's features.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

ApplicationDrivenLearning.jl - A Framework for Decision-Focused Learning in Julia

Discover how to optimize forecasts for specific applications using Julia's ApplicationDrivenLearning.jl package with JuMP decision models and Flux forecasting capabilities.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Typstry.jl - The Julia to Typst Interface

Discover how to integrate Typst typesetting with Julia using Typstry.jl for seamless document generation, formatting, and rendering with custom string macros and CLI integration.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Julia in Nginx - Embedding Julia in Web Server Applications

Explore how to embed Julia in nginx web servers, replacing Lua with Julia's powerful ecosystem for building web gateways, proxies, and WAFs through a custom C module implementation.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Lead, Follow, or Get Out of the Way - Julia and Threaded Python

Explore Julia-Python interoperability challenges, threading complexities, and practical workarounds for creating high-performance Python wrappers around Julia packages.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Introducing Quarto's Native Julia Engine - Easier, Faster, Better

Discover Quarto's new native Julia engine that eliminates Python dependencies, enables expandable cells, and supports R integration for seamless computational document creation.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Improving Julia's Scheduler - One Task at a Time

Dive into Julia's task-based parallelism scheduler improvements, exploring user-space scheduling, performance optimizations, and writing cooperative code for better efficiency.

• YouTube
• 28 minutes
• Self-Paced
• Free Video