Reasons and advantages of python suitable for artificial intelligence

With the help of AI and data science, Python has climbed to the top position of the programming language ecological chain. It can be said that Python and AI have basically been tightly bound in one case. Why should python language be used in artificial intelligence development? This site believes that it is based on the following reasons:

Simple and efficient

As a programming language, Python is a suitable choice for programmers to work related to AI and machine learning. Simple and beautiful, with high development efficiency, Python language has been favored by more and more companies, and many companies have begun to choose Python for the development of website Web, search engine, cloud computing, big data, artificial intelligence, scientific computing and other directions.

Strong portability

Python wants to see a better person create and constantly improve. Because of its open source nature, Python has been ported to many platforms. If you are careful not to use system-dependent features, all of your Python programs can run on any of the following platforms without modification.

Huge standard library

Python supports both process-oriented functional programming and object-oriented abstract programming. In a procedure-oriented language, programs are built from procedures or simply functions of reusable code. In object-oriented languages, programs are built from objects that combine data and functionality. Compared with other major languages such as C + + and Java, Python implements object-oriented programming in a very powerful and simple way, and the standard library is really huge.

Some languages rely too much on the commercial support of the gold owners behind them. When they are good, the scenery is infinite. Once they are put into limbo, even survival is a problem. More languages, just after achieving one point of success, can't wait to become all-around champions, and desperately extend their tentacles in all directions, especially in enhancing their expression ability and improving their performance. They are often too active, and they will not hesitate to change their core languages beyond recognition, and will eventually become behemoths that no one can control.

In contrast, Python is a successful example in the design and evolution of modern programming languages. In the final analysis, the reason why Python is so clear in strategic positioning and firm in strategic adherence is that its community has built an exemplary decision-making and governance mechanism. Therefore, Python, who is firmly at the top of artificial intelligence language, deserves its name.

Netizen's point of view:

Why artificial intelligence uses Python

This is a misunderstanding. The core algorithm of artificial intelligence completely depends on C/C + +, because it is computationally intensive and requires very fine optimization. It also needs interfaces such as GPU and special hardware, which only C/C + + can do. Therefore, in a sense, C/C + + is the most important language in the field of artificial intelligence.

Python is the API binding of these libraries. Python is used because of the glue language characteristics of CPython. To develop a cross-language interface from other languages to C/C + +, Python is the easiest, which is much lower than the threshold of ffi of other languages, especially when using Cython. Many ffi in other languages can only import the function entry point of C, and most of the complex data structures can only be put together manually with byte arrays. If you need callback function input, there is nothing you can do. C API of CPython is bidirectional fusion, which can directly expose encapsulated Python objects, allow users to introduce new features by inheriting these custom objects, and even call Python functions from C codes (of course, there are also 1 certain conditions). However, this is also an obstacle for JIT interpreters like PyPy.

Moreover, Python has been an important tool for scientific calculation and data analysis in history, with the foundation of numpy. Because the industry is similar, Python will be preferred when choosing API binding language. At the same time, reusing basic libraries such as numpy not only reduces the development workload, but also facilitates practitioners to get started.