# Julia Language in Machine Learning: Algorithms, Applications, and Open Issues

arXiv: https://arxiv.org/abs/2003.10146 · HTML: https://mlsanity.com/paper/2003.10146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Modeling chaotic Lorenz ODE System using Scientific Machine Learning](https://mlsanity.com/paper/2410.06452/) (2024)
- [The State of Julia for Scientific Machine Learning](https://mlsanity.com/paper/2410.10908/) (2024)
- [A comparative study of NeuralODE and Universal ODE approaches to solving Chandrasekhar White Dwarf equation](https://mlsanity.com/paper/2410.14998/) (2024)

## Abstract (arXiv)

Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper summarizes the related research work and developments in the application of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use of the Julia language in machine learning.