Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
It addresses the need for a programming language that combines ease of use with high performance for machine learning practitioners, but it is incremental as it reviews existing work rather than introducing new methods.
This paper surveys the use of the Julia programming language in machine learning, highlighting its balance of efficiency and simplicity compared to languages like Python and C/C++, and summarizes existing algorithms, applications, and open issues.
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.