Julia Language: A Programming Language for Machine Learning

The announcement of the Julia programming language was made in the summer of 2018, after nearly a decade of work. Version 1.0 became available to everyone under an open source license. But what exactly is the Julia language?

The Julia programming language is an incredibly ambitious programming language developed by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman. Tired of having to switch between Matlab, Lisp, Python, Ruby, Perl, Mathematica, R, and C, this group decided to develop a special programming language that would be useful for scientific computing, machine learning, data mining, linear algebra, parallel and distributed computing.

Want to learn more about the Julia programming language? Keep reading, because we will explore the key aspects of one of the most used machine learning languages. Get your laptop ready!

What is the Julia programming language?

The Julia programming language was designed to combine the advantages of a dynamic language with the performance of a compiled language. This is achieved, in particular, through the use of a JIT (just-in-time) compiler based on LLVM (Low Level Virtual Machine), which allows the generation of code that fully corresponds to machine language.

The Julia programming language is very attractive to data scientists, statisticians, and financial analysts because it is easy to learn and has high performance. Its main problem at the moment is that it is a young language, only two years old, and therefore it does not offer the number of libraries and resources that other languages like R, Python, or Matlab can provide.

In 2015, Julia developers founded Julia Computing, which currently manages the open source repositories. This infrastructure also develops commercial products that facilitate the use and implementation of the Julia language.

As we noted above, Julia features an advanced compiler, several mechanisms for parallel and distributed execution, and an interesting library of mathematical functions.

Although it is still a relatively new programming language, the community around it has already grown and continues to expand through the creation and distribution of external packages via Julia's integrated package manager. Thus, Julia has become a collaboration between the Python community and the growing Julia community.

Features of Julia

• Allows users to define function behavior through different combinations of argument types
• Is a dynamic type system
• Optimal performance approaching that of statically compiled languages like C
• Built-in package manager
• Lisp-like macros and other tools used for metaprogramming
• Call Python functions using the PyCall package
• Call C functions directly without using special APIs
• Powerful command-line capabilities for managing other processes
• Designed for parallel and distributed computing
• User-defined types work as fast and compactly as standard built-in types
• Automatic generation of efficient and specialized code for different argument types
• Elegant and extensible conversion and promotion for numeric and other types
• Efficient support for Unicode, including but not limited to UTF-8
• Free and open source license

Application of Julia in Machine Learning

Speaking of the application of Julia in machine learning, the Julia programming language has gained enormous popularity due to its high-speed machine learning packages and its very expressive and convenient syntax for developing machine learning.

Julia is a high-level programming language created at the Massachusetts Institute of Technology, which rivals Python in syntax and C in performance.

Regarding data analysis, the Julia programming language is quite functional despite being a relatively new language. However, thanks to a very active community around Julia, which also merges with communities related to other codes, more and more libraries and resources are emerging to facilitate its use.

This dynamic, high-level programming language is designed for high-performance numerical analysis and computational science. The standard library written in Julia includes:

• Best open-source libraries in C and Fortran for linear algebra
• Random number generation
• Signal processing
• String processing

In addition to all this, thanks to collaboration between different communities, Julia offers a powerful browser-based graphical interface for Julia.

Framework for Artificial Intelligence and Machine Learning

The Julia programming language comes with Flux—a framework or library (depending on the authors, it's either one or the other) for machine learning and artificial intelligence.

This tool has a very intuitive interface and, despite that, is one of the most flexible frameworks because it can be easily integrated into other libraries or even used simultaneously on multiple cores.