Julia language in machine learning: Algorithms, applications, and open issues

被引：27

作者：

Gao, Kaifeng ^{[1
]}

Mei, Gang ^{[1
]}

Piccialli, Francesco ^{[2
]}

Cuomo, Salvatore ^{[2
]}

Tu, Jingzhi ^{[1
]}

Huo, Zenan ^{[1
]}

机构：

[1] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China

[2] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy

来源：

COMPUTER SCIENCE REVIEW | 2020年 / 37卷

基金：

中国国家自然科学基金;

关键词：

Julia language; Machine learning; Supervised learning; Unsupervised learning; Deep learning; Artificial neural networks; COMPUTER VISION; OBJECT DETECTION; THINGS IOT; INTERNET; MODEL; ICA; EXTRACTION; TOOLBOX; IMAGERY; TRENDS;

D O I：

10.1016/j.cosrev.2020.100254

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 applications 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. (c) 2020 The Authors. Published by Elsevier Inc.

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页数：13

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