论文标题
机器学习中的朱莉娅语言：算法，应用和开放问题
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
论文作者
论文摘要
机器学习正在推动科学和工程领域许多领域的发展。一种简单有效的编程语言可以加速机器学习在各个领域的应用。当前，最常用于开发机器学习算法的编程语言包括Python，Matlab和C/C ++。但是，这些语言都没有很好地平衡效率和简单性。朱莉娅语言是一种快速，易于使用且开源的编程语言，最初是为高性能计算而设计的，可以很好地平衡效率和简单性。本文总结了朱莉娅语言在机器学习中应用的相关研究工作和发展。它首先调查了朱莉娅语言开发的流行机器学习算法。然后，它研究了用朱莉娅语言实现的机器学习算法的应用。最后，它讨论了在机器学习中使用朱莉娅语言时出现的开放问题和潜在的未来方向。
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.
