Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
Developments in Julia Programming Language
This research summary is just one of many that are distributed weekly on the AI scholar newsletter. To start receiving the weekly newsletter, sign up here.
Having been around for several years, Python has evolved to become one of the most loved data science programming languages. However, it’s not the only programming language establishing itself in the emerging field. There’s a new entrant — Julia — which is likewise making some noteworthy strides and increasingly getting fans.
Julia was originally developed by a team led by MIT computer scientist Alan Edelman and three others who sought to create a free language that was both high-level and fast. The language is fast, easy to learn and use, and open source and is mainly used for scientific computing, machine learning, data mining,
Julia Language: Unique Features
- Julia is compiled and not interpreted making it a fast programming language
- It has a straightforward syntax (similar to that of Python) that can be easily understood by beginners.
- It is dynamically typed — you don’t have to specify or sign the variables
- Supports metaprogramming — can be used to create programs with unique codes.
- Can access libraries belonging to other programming languages including C, Fortran, and Python.
Julia Language in Machine Learning
Where does Julia stand in Machine Learning? This article is about a recently released research paper by a group of scholars with the School of Engineering and Technology China and University of Naples Federico II, Naples, Italy about the use of Julia in ML, current applications, algorithms, and challenges.
According to the paper, Julia is rapidly becoming a highly competitive language in data science and general scientific computing. Originally designed for high-performance scientific computing and data analysis, Julia is as easy to use as R, Python, and MATLAB.
Specifically, the paper systematically reviews and summarizes the development of the Julia programming language in the field of machine learning by focusing on the following three aspects:
(1) Machine learning algorithms developed in the Julia language.
(2) Applications of the machine learning algorithms implemented with the Julia language.
(3) Open issues that arise in the use of the Julia language in machine learning.
The research also states that Julia is widely used in seven popular machine learning research topics: pattern recognition, NLP, IoT data analysis, computer vision, autonomous driving, graph analytics, and signal processing.
Open Issues in Julia Programming Language
The greatest challenge with Julia is that there are far fewer available application packages than there are for other high-level languages such as Python. It is also a young and developing language. It is being updated and there has been a recent version making it relatively stable, but there are still many issues to be solved such as a lack of stable development tools, interfacing with other languages, and a limited number of third-party packages.
If you have been looking for a good and comprehensive reference for Julia’s development in the field of machine learning, this research will sort you out. We believe that with the gradual maturing of the Julia language and the development of related third-party packages, the Julia language will be a highly competitive programming language for machine learning, researchers conclude.
Julia Version 1.5 is Out
Just months after releasing Julia Version 1.4, Julia version 1.5 is out: Lots of new features, better performance.
Read the paper: Julia Language in Machine Learning
Thank you for reading! I value your comments and shares and would love to connect on Twitter, LinkedIn, and Facebook. For updates on the most recent and interesting Machine Learning research papers out there, subscribe to AI Scholar Weekly. Please 👏 if you enjoyed this article. Cheers!
