Shout out to Machine learning developers as Julia’s fanbase is bubbling out from its shed.
In the ever-evolving world of technology, Julialang made a debut to transform all the genres of machine learning development. Slowly & steadily Julia language attained explicit prominence among top programming languages. So, have you ever imagined that what’s so special about Julia? Whether you’re a squad of Pythonists or Rubyists, nothing matters if you came across a power-packed supplant with characteristics of all programming languages that you’ve been using for so long. We’re not questioning the potential of other languages but we should agree on a fact that an open-source language powered with a speed of C & dynamism of Ruby will do justice to the present-day arena of Machine learning development.
It might not sound much surprising news for tech lovers that Julialang secured its place on the 35th level but yes, your exhilaration will take a jump knowing that it aroused up on the charts from 50th place among the conquers like Java or Python. Today, the immense popularity of Julia language beats Kotlin, the most preferred language for Android app development.
According to Professor Alan Edelman, co-creator of Julia programming language, “It was a kind of thought of as a law of physics if you will, a kind of law of nature, that you can have one or the other, but that in some ways it would be impossible to have both.” Julia emerged as the first-class language that gears up for the combined speed of C with integrated usability of Python, the dynamism of Ruby, the mathematical prowess of MatLab, & the statistics as R. It is something that is absolutely simple to learn & quick to compile.
If you don’t want to settle for less then you should give a try to Julialang for Machine learning development & there are some reasons for it. Before you move on to them, let’s give a brief on Julia programming language and statistics to favour its use:
Top tech giants like Amazon, Google Maps, Netflix, Siri, & more come under the hats of machine learning technology. With a plethora of data-driven prediction models & solutions, the innovation of Julia adds fuel in the fire in terms of speeding up the optimization process for increased ROI. ML is predicted to achieve excellent growth rate with a CAGR of 43% between the year 2019-24 in combination with Julialang.
Since its launch in 2012, Julia came out as a high-level general-purpose open-source programming language developed to perform high-end computations. If you’re new to this programming language then this brief introduction will definitely keep you engaged for the next few minutes. Julia stockpiles feature like multiple dispatch, built-in package, distributed computing, multiple dispatch, & other add-ons. Though it merges C, Python, MATLAB, Ruby, & R, it gained popularity within a shorter span of time, particularly for this reason.
When it comes to choosing Julia over others, there are certain factors to favour your choice to go for this programming language for Machine Learning development and here are they:
Every new technology or software or tool comes with a huge scope of upgrades & updates. Julia emerged as a major programming language that yet discloses some more enhancements in the future. The ecosystem ruled by this programming language brings out some tools that are marked as Julia plugins for multiple IDEs, Visual Studio, Atom, & VS code. Though every programming language is released on the verge of the work-in-progress model, some of its mind-boggling updates are yet to come in the near future. Watch out our latest blogs to stay updated on machine learning development!