- Suffixtree ERP
The answer can be summarized in one word: “Ecosystem” - which means, Python has all the best packages and stuff for numeric computing.
However, why is the Python ecosystem better? What leads to a better ecosystem?
It is the fact that Python was designed from the origin so extension modules could be written in C - Not just the ability to call C libraries, but to literally write 3rd-party modules that perform accurately like Python modules in the runtime. This is also possible in Ruby, but Ruby was started up for the creation of extension packages somewhat later than python. In Python, the creation of C extensions is common, and Python existed originally very popular with expert C programmers who used the language as high-level “glue” for their fast C libraries.
This was also how it got its origin in numeric computing: Scientists who knew C wrote extensions using C and Fortran for doing advanced vector and matrix manipulations that now serve as the foundation for Python’s numeric stack. It had those libraries long before any other dynamic languages could do these kinds of things efficiently, and it’s been mounting up from that foundation for a long time.
The culture of C extensions around Python is the main reason it has become more popular than Ruby and all other dynamic languages, especially in this domain. Some might argue that Python syntax is easier to learn, but Ruby syntax is also very nice.
Advantages of Python over Ruby:
Ruby has smart syntax. Python has pure syntax.
Ruby has method aliases. Python does not allow a string to capitalize itself.
Ruby uses Ruby methods its classes to extend Ruby. Python has decorators so you can write functions that return functions to create a new function.
Ruby has strict object-oriented encapsulation. Python is laid-back about objects because you probably know what's going on inside them anyway.
Ruby lets you leave off parentheses so you don't miss objects having attributes too much. Python will let you mix tabs and spaces for indentation, but passive-aggressively mess up your scoping as punishment.
Ruby has seven kinds of closures. Python has one, in the unlikely case, a list comprehension won't do.
Ruby's C implementation is a clutter of support for language-level flexibility. Python's C implementation is so clean you get the unsettling thought that you could probably write Python using C macros.
Ruby supports metaprogramming for cases when programmers find it more descriptive. Python supports metaprogramming for cases when programmers find it necessary.
Ruby is expressive. Python is direct.
Ruby is English. Python is Esperanto.
Ruby is a verse. Python is prose.
Ruby is beautiful. Python is useful.
So what makes Python the best choice for Artificial Intelligence?
Extensive range of libraries and frameworks:
One of the features that make Python such a popular choice in general, is its excess of libraries and frameworks that facilitate coding and save development time.
Python is renowned for its compact, simple code, and is virtually unrivalled when it comes to ease of usability and simplicity, especially for fresh developers.
Python’s simple syntax anticipates that it is faster in development than many programming languages, and enables the developer to promptly test algorithms without having to execute them.
The profusion of support:
Python is an open-source programming language and is supported by a number of resources and high-quality documentation. It further boasts a huge and vibrant community of developers ready to render advice and assist throughout all stages of the development process.
AI is having an intense effect on the society we live in, with distinct applications developing all the time. Ingenious developers are adopting Python as their go-to programming language for the benefit that makes it particularly suitable for machine learning and deep learning projects.
While different programming languages can also be used in AI projects, there is none getting away from the fact that Python is at the cutting edge, and should be provided significant reflection. This is why you should definitely consider Python for your AI contour.
This blog is originally published in myTectra