Redacción Tokio | 19/12/2022
The announcement for the launch of the Julia programming language took place in the summer of 2018, after almost a decade of work. Version 1.0 was made available to everyone through an open source license. But what is Julia language exactly?
The Julia programming language is a tremendously 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 of people decided to develop a specific programming language that would be useful for scientific computing, machine learning, data mining, linear algebra, parallel computing, and distributed computing.
Want to learn more about the Julia programming language? Keep reading, because we are going to go through the main keys to one of the most used languages in machine learning. Get your notebook ready!
What is the Julia programming language?
The Julia programming language has been designed to gather the advantages of a dynamic language with the performance of a compiled language. This is achieved in part thanks to the use of a JIT (just-in-time) compiler based on LLVM (Low Level Virtual Machine) that allows the generation of code that is completely machine native.
The Julia programming language is very attractive to data scientists, statisticians, and financial analysts because it is easy to learn and offers great performance. Its biggest problem, right now, is that it is a young language, as it is only two years old and so it doesn’t offer the quantity of libraries and resources that other languages like R, Python or Matlab can provide.
In 2015 the Julia designers founded the Julia Computing company, which is currently in charge of maintaining the open source repositories. This infrastructure also develops commercial products that facilitate the use of Julia language and its implementation.
As we’ve mentioned above, Julia presents an advanced compiler, several mechanisms for parallel and distributed execution, and an interesting library of mathematical functions too.
Even though it’s still a relatively new programming language, the community around it has grown and is increasingly generating contributions with the creation and distribution of external packages through a specific Julia integrated manager. As such, Julia has turned into a collaboration between the Python communities and the Julia community that has grown around it.
Julia language features
- Allows users to define function behavior through various combinations of argument types
- Presents a dynamic typing system
- Optimal performance, approaching that of statically compiled languages like C
- Integrated package manager
- Lisp-type macros and other tools used for meta-programming
- Python calling functions using the PyCall package
- Call C functions in a direct way, without the need for particular APIs
- Powerful command line features to manage other processes
- Designed for both parallel and distributed computing
- User-defined types work just like standard built-in types in terms of speed and compactness
- Automatic generation of efficient and specialized code for different types of arguments
- Elegant and extensible conversions and promotions for numeric types and other types
- Efficient Unicode support, including but not limited to UTF-8.
- Free, open source license
Julia language applications in Machine Learning
When it comes to the applications of Julia language within the field of Machine Learning, the Julia programming language has become enormously popular thanks to its high-speed machine learning packages and a highly expressive and useful syntax for developing machine learning.
Julia originates straight out of MIT, a high-level programming language that presents a syntax as friendly as Python and performance as competitive as C.
Regarding data analysis, the Julia programming language is perfectly functional, even if it’s a relatively new programming language. However, because of the very active community around Julia language, which also merges with communities related to other codes, more and more libraries and resources exist and facilitate its implementation.
This dynamic, high-standard programming language is designed to address the needs of high-performance numerical analysis and computational science. The base library written in Julia language includes:
- Best open source C and Fortran libraries for linear algebra
- Generation of random numbers
- Signal processing
- String processing
On top of all this, thanks to the collaboration between the multiple communities, Julia presents a powerful browser-based GUI for Julia.
Framework for AI and machine learning
The Julia programming language comes along with Flux, a framework or library (depending on the authors, it is one or the other) for Machine Learning and Artificial Intelligence.
This tool provides a highly-intuitive interface and, despite this, it is one of the most flexible frameworks out there, since it can be easily integrated within other libraries or even employed simultaneously on several kernels.
Become an expert in Machine Learning
The Julia programming language is useful not only for data scientists, but for anyone with an interest in the field of statistics or machine learning. In fact, the success or failure of Julia language is now in the hands of the users, who may or may not decide to adopt this interesting project.
Whatever happens, all signs now seem to indicate that Julia language presents a great potential to become an established option and succeed. For this reason, it is essential to master it if you’re considering a specialization in Machine Learning.
While this is one of the most promising career fields for the future, one for which you’ll surely need specialized training. Learn more about this and other tools in Tokyo School Machine Learning Specialization course. Request more information now and become the professional you’ve always wanted to be. We can’t wait to meet you!