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What are the core features of Julia programming language?

Julia programming language offers several features, including a built-in package manager, multiple levels of dispatch, and extensive support for parallel and distributed Julia computing. As a perfect blend of C, Ruby, Python, R, and MATLAB, it has become popular in the market. Owing to all these features, this technology has become a core element among various business models belonging to companies like NASA, Disney, and Tencent.

People practicing Julia’s latest version are choosing it because of its performance, speed, superiority, and ease of use. This goes well, especially for parallel and distributed Julia computing, including TPUs and GPUs. With hundreds of AI and ML projects available in Julia, it is time to envision several mind-blowing Julia’s latest version projects in 2020.

What are the advantages of Julia vs Python?

Compilation
Julia development is nothing but a compiled language. Owing to this, it performs faster in comparison to any interpreted language. We cannot categorize Julia development as strictly statically typed. It leverages JIT (Just in Time) compilation for inferring each code variable, and it can achieve the speed of C and Go.

Parallelism
You can run your code in parallel using Python while leveraging every CPU core available on the system. You can also import modules with it.

Libraries
Even Though you have a small number of Julia libraries, you can efficiently run Python libraries on Julia. You can use C/Fortran libraries and run it directly on Julia.

Type Checking
Similar to Python, Julia is a dynamically-typed language. You can specify types for a variable easily with Julia.

Multiple dispatches
Julia interpreter can check the argument type whenever you call the reverse. You can efficiently dispatch the version it matches the style.

Array indexing
Arrays in Julia are 1-indexed. This makes Julia more intuitive and user-friendly for users with zero-indexed languages.

What are its applications of, Julia?

Julia consists of numerous applications. A few of them are here:
Web programming – both at the client and server end,

  • Hardware integration 
  • AR-powered software and tools
  • Deep Learning
  • Risk management
  • Coding UIs
  • Building relevant healthcare-based solutions
  • Genomics and precision medicine
  • Self-driving cars and 3D printing

Pattem Digital is here to support all your Julia development services-related requirements. Want to know more about Julia vs Python? You can rely upon them if you have any requirements related to Julia from documentation to maintenance. Talk to the experts if you need further help with Julia development services. Let us know if you can support any of the requirements at business@pattemdigital.com. We are here to stand and keep you!

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