Julia, a comparatively advanced software language, has recently experienced widespread popularity in ML applications. It has equivalent speed and functionality to C programming, as well as the ability to perform analytical and numerical computations. It is found to be more efficient than Python Programming.

Julia was created out of the realization that while scientific computing has traditionally demanded high efficiency, domain experts generally program in a slower, more complex language on a daily basis. Although Julia’s developers recognize that programmers prefer dynamic languages for some applications for a variety of reasons, they still wanted to use modern language layout and compiler methods to build a specific environment that is strong enough for prototyping but efficient enough for performance-intensive applications.

Julia’s main characteristic is that it was built from the ground up for high-performance programs that demand faster data processing, such as machine learning and scientific computing.


Julia was designed from the start for scientific and numerical computation. Thus it’s no surprise that Julia has many features advantageous for such use cases:

Julia is fast. Julia’s JIT compilation and type declarations mean it can routinely beat “pure,” unoptimized Python by orders of magnitude. Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.

Julia has a math-friendly syntax. A major target audience for Julia is users of scientific computing languages and environments like Matlab, R, Mathematica, and Octave. Julia’s syntax for math operations looks more like the way math formulas are written outside of the computing world, making it easier for non-programmers to pick up on.

Julia has automatic memory management. Like Python, Julia doesn’t burden the user with the details of allocating and freeing memory, and it provides some measure of manual control over garbage collection. The idea is that if you switch to Julia, you don’t lose one of Python’s common conveniences.

Julia offers superior parallelism. Math and scientific computing thrive when you can make use of the full resources available on a given machine, especially multiple cores. Both Python and Julia can run operations in parallel. However, Python’s methods for parallelizing operations often require data to be serialized and deserialized between threads or nodes, while Julia’s parallelization is more refined. Further, Julia’s parallelization syntax is less top-heavy than Python’s, lowering the threshold to its use.

Julia is developing its own native machine learning libraries. Flux is a machine learning library for Julia that has many existing model patterns for common use cases. Since it’s written entirely in Julia, it can be modified as needed by the user, and it uses Julia’s native just-in-time compilation to optimize projects from inside out.


1. Signal Processing

2. IoT data analysis

3. Computer Vision

4. Autonomous Driving

5. Natural Language Processing

6. Pattern Recognition

7. Graph Analytics

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1. Getting Started With Julia (Udemy)

2. Julia Scientific Programming (Coursera)

3. Introduction to Julia (JuliaAcademy)

4. Mastering Julia 1.0 (Packt)

5. Coding for non-programmers in Julia

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2. https://voxeu.org/article/which-programming-language-best-economic-research

3. https://www.hpcwire.com/2021/01/13/julia-update-adoption-keeps-climbing-is-it-a-python-challenger/

4. https://arstechnica.com/science/2020/10/the-unreasonable-effectiveness-of-the-julia-programming-language/

5. https://www.zdnet.com/article/programming-languages-julia-users-most-likely-to-defect-to-python-for-data-science/

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