Julia is one of those languages suitable for machine learning (ML) as well as deep learning (DL). Started in 2015, it is a low-level systems programming language. Like all other languages, Julia was introduced to overcome the shortcomings of the predecessors.
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Features of Julia
Julia can handle mathematical expressions with great ease and speed. It has numerous libraries and packages for building ML solutions. It offers great support for TensorFlow and MXNet frameworks. Since it is a new language, it contains the benefits of Python, R, SAS, Matlab and C. TensorFlow is much easier to use with Julia code. Julia’s syntax is similar to Python and it’s speed is equivalent to C++. String processing as swift as Perl, powerful as Matlab for linear algebra; statistics as lucid as R.
Some of the data structures are: Vector, Matrix, String and Dictionary. It is interoperable with many languages like Fortran, C and it’s packages also supports markup languages like HTML, JSON, XML, etc.
It is very rich when it comes to ML packages and libraries. Due to its built-in package manager, it already has over 1900 registered packages. Some vital machine learning libraries are quite evolved compared to other languages namely, Mocha for deep learning, Orchestra for optimization. Data-frames are in a much evolved version here.
It supports both dynamic typing and static typing as well. When a function is called in Julia, the arguments are already known. It generates native machine code directly, before a function is first run. The compiler takes care of it.
It can interface with other languages too. Thus, data sharing is also possible.
When types are omitted, it can allow values of any type to be entered.
Julia uses multiple dispatch as a paradigm, allowing developers to change the behavior of functions based on run-time activity.
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Applications of Julia
Julia’s REPL (Read/Evaluate/Print/Loop) provides easy access to special characters, such as Greek alphabetic characters, subscripts, and special maths symbols. Julia supports parallel computing at all levels. For these reasons, it has by now gained popularity in many sectors.
One of the biggest insurance firms of UK, Aviva, uses Julia in their complex Monte Carlo risk models. The Federal Aviation Authority (FAA) in USA has a system to sense all nearby air-crafts. They now plan to use Julia for this aircraft-sensing technology. NY Fed also uses it to perform to perform the humongous modelling in ML for the US economy.
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Future Prospect
Julia lets write UIs, statistical compilation of code, or even deployment on a web-server. It also has powerful shell-like capabilities for managing other processes. It provides Lisp-like macros and other meta programming facilities. According to a 2019 survey, 73% of users and developers use Julia for research. The most popular fields are statistics, engineering, machine learning and computer science. Saying those, it is still in it’s niche stage and has a lot to improve and develop in future.
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