Julia Language in Machine Learning: 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 inclu...

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Bibliographic Details
Published in:arXiv.org 2020-05
Main Authors: Gao, Kaifeng, Mei, Gang, Piccialli, Francesco, Cuomo, Salvatore, Tu, Jingzhi, Huo, Zenan
Format: Article
Language:English
Subjects:
Algorithms
Machine learning
Programming languages
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2003.10146