Why artificial intelligence needs to learn python

Artificial intelligence, as one of the hottest terms in the IT industry today, has attracted attention from all walks of life. Various IT training institutions have also joined this trend and launched artificial intelligence courses. Attentive friends may have discovered that no matter which training institution you are in, artificial intelligence and Python are always inseparable.

# Why you must use Python to learn artificial intelligence? Let’s briefly analyze it here. (Recommended learning: Python video tutorial)

1. Life is short, I use Python: simple, efficient, easy to get started

is discussing Before why choose Python, we must first know what Python is.

Python is an object-oriented, interpreted computer programming language. It is designed to be elegant, clear and simple. Compared with the Java language, which is also object-oriented, functions, modules, strings, and numbers are all objects for Python, unlike Java where there are basic types.

Some friends may ask, as a scripting language, Python is not as fast as Java and C, so why should you choose Python?

The core calculations of artificial intelligence are all written in C language, and Python only writes logic. It’s not that C language can’t write upper-level logic, it’s just that the amount of code is too large and the development efficiency is low. The running speed can be improved through hardware upgrades, but the development speed cannot be improved by stacking manpower. For current AI applications, rapid development is more effective than rapid execution.

2. Python has a rich and powerful library, nicknamed the glue language

We mentioned above that the real calculation of artificial intelligence is completed by the C language. If you want to write the logic of artificial intelligence, you need an interface from other languages ​​​​to C language. Python is the lowest and easiest one. Moreover, Python has historically served as an important tool for scientific computing and data analysis. Basic libraries such as numpy not only reduce the development workload, but also make it easier for practitioners to get started.

3. Python has a wide range of application fields. It supports aerospace system development, down to small game development, and is almost omnipotent.

Python is a universal language and can be used anywhere, but the best application scenarios are those that pursue development speed and do not care much about operating efficiency.

The biggest application of Python now is the web backend, and then there are Linux system management, rapid prototype development under various platforms, gadget writing, or as a glue language to schedule things written in other languages.

For more Python related technical articles, please visit the Python Tutorial column to learn!

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