Is Machine Learning in Julia significant?

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Is Julia revolutionizing the landscape of Machine & Deep Learning

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All programming languages were built to serve a certain purpose. The ascent of Julia was for the field of data analytics, and Machine Learning use cases. Python was definitely not built for ML, but timely advancement and package developments made Python a favorable choice for engineers.

Data analytics deals with massive data. Python is a programming language we know is used extensively for processing, manipulating & wrangling data, machine learning, and everything else. Although R is also used to some extent, it is no competition to the popularity python.

But Python has its shortcomings, and to overcome those, a bunch of developers seeded Julia programming language in 2009, aimed exclusively for data processing. It was in 2012 that Julia was released into the public domain. Initially, it was patchy, there were individual packages for a single problem, and some of the issues were unaccounted for. Also, not all data types were suited for processing and required conversion. There was extreme chaos in packages with poor documentation.

However, Julia developers improvised her strategically, and things leaped with the stable release of Julia 1.0. It is now seen as a viable and apt tool to revamp mathematical and scientific computing in machine learning. Its magical tool Genie has earned those special brownie points for managing the environment for Julia. The documentation is evolving and getting more lucid to understand.

What is Julia?

As attractive as it sounds, Julia is the new crush of machine learning lovers. Julia is a programming language specifically built for data analysis and machine learning. It is open-source and was incepted as a high-performance alternative to Python.

Julia is a compiled language using the LLVM compiler framework. It aims to match the speed of C, yet it is syntactically convenient and iterative for the user. The command line is as user friendly as Python. The syntax is shorthand but also easily understood. The language is dynamically typed and can call libraries of Fortran, Python, and C. We see the advanced feature of metaprogramming. Programs written in this language are self-converging, they can modify themselves and even generate new codes.

What gives Julia the edge?

Julia was born to be what it is claiming to be. Undoubtedly it is meant to minimize the hassles of data processing and analysis. Here we analyze the strengths of the language.

Speed

Julia’s LLVM compiler framework gives it the required speed while dealing with massive data. This function is a pure edge on the python interpreter. Python can take help from external sources to make it fast, but for Julia, it is inbuilt.

Mathematical operations

Julia has easy-going syntaxes of mathematical formulas. It is as friendly as writing a math formula with pen and paper, hence non-programmers can easily benefit from this.

Memory management

Julia has automatic memory management. Its memory management is as brisk as that of Python. The makers have well kept in mind that perks of using Python are assimilated into Julia’s conveniences. There is also a provision of manual control over the garbage collection.

Fast computing

Scientific computation are heavy on the processor and can be sped up using the full capacity of the available CPU resources. This is where parallelism comes into picture and Julia uses all available cores within a CPU to run processes parallelly.

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Where does Julia stand?

We cannot deny the popularity Python enjoys in the Machine learning fraternity, and it has managed to hold this place for a long time. According to PYPL, Python is still the most Google-searched language. Julia has started to catch up, currently at the 27th position.

Source: PYPL

Given the theoretical advantages Julia is offering, it would still be immature to conclude what the future holds for it.

Python is mature. Julia, incepted in 2009, is quite young. By contrast, Python has been around for almost 30 years, and researchers have done more than sufficient work to develop packages for smooth scientific and mathematical computations. It has been engineered and reengineered a million times to reach this level of maturity. Python has somewhat become a cult. However, learners who want to break the mold and do something new are tweaking around Julia for smoother and improved variations.

There is a feature of Pycall in Julia through which it connects to the python platform; however, the interpretation is quite slow and reduces the speed of computing.

Programmers used to Python are a bit wary of the new language. It is quite apparent that switching from a language with which they adhered for long can be repelling. Some even find the language not as simple as Python.

However, for a start, Julia can help fill the gap between Python and Scala. Julia’s charm lies in the significant speed which it carries, but it often ends up calling packages from Python, which is a deterrent to speed. Having its own alternative of Scikitlearn and visualization packages may help Julia play on the front foot.

Conclusion

There is no hurry, Julia has to take it’s time to bear fruit. It can be utilized for all types of use cases with the promises it wants to meet only if the code is pure Julia. The foundation for fast mathematical and scientific computing is already sown and it has made quite a buzz. However, Python enthusiasts are not left behind. Programmers of both genres are continuously improvising in the language codes. For end-users, it would be quite interesting to watch the trajectory not only for Julia but also for Python.

Until Julia reaches a significant level of maturity, it is in no way any competition to the mighty Python, and revolutionizing Machine learning through Julia is a far fetched concept as of now.

For more learning reference on Julia visit https://docs.julialang.org/en/v1/ & https://julialang.org/

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