Resources

Learning Materials

Basics

• Have a glance
  • MATLAB-Python-Julia cheatsheet
  • Julia cheatsheet
  • The Julia Express
• Thoroughly understanding
  • Julia Documentaion (official doc also includes advanced topics)
  • Think Julia

Courses

• QuantEcon with Julia
  • This is a course to Quantitative Economics, and contains well-written tutorial for Julia beginners.
• MIT’s 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. (videos, book)
  • A technical Julia course for parallel computing and scientific machine learning. This couse can teach Julia developers to write efficient and parallelized code.
• MIT 18.S191/6.S083/22.S092 Introduction to Computational Thinking
• A Deep Introduction to Julia for Data Science and Scientific Computing

Community

• Discourse
• Forum

Packages

• Survey of Julia packages

News

• Julia Computing

Styling guide

• SciML style
• Performance Tips

Books

• Hands-On Design Patterns and Best Practices with Julia: Proven solutions to common problems in software design for Julia 1.x
  • Essential knowledge for developing packages
• Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for optimization. Mit Press, 2019. [link]
  • Graduate-level optimization book with Julia examples
• Kochenderfer, M. J., Wheeler, T. A., & Wray, K. H. (2022). Algorithms for decision making. MIT press. [link]
  • Markov process with Julia