A young programming language for machine learning is on the rise and could be soon gunning for Python.
Of the many use cases Python wraps, data analytics has become perhaps the largest and most significant. The Python ecosystem is laden with libraries, tools, and applications that make the work of scientific computing and data analysis speedy and suitable.
But for the developers behind the Julia language — intended specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”. It is a fresh approach to technical computing. But Python isn’t fast or convenient enough. It’s a trade-off, good for some parts of this work but dreadful for others.
Now the question arises - What is Julia language?
Created in 2009 by a team of four-person and unveiled to the public in 2012, Julia is destined to address the drawback in Python and other languages and applications used for scientific computing and data processing.
The official website describes Julia as: “A high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace.”
Here are some of the ways Julia implements:
Julia supports Meta-programming. Julia programs can generate other Julia programs, and even modify their own code, in a way that is redolent of languages like Lisp.