Why artificial intelligence uses python

Compared with other languages, python’s biggest advantage for artificial intelligence is its scalability and embeddability. This is why it is called "glue language" by programmers.

Advantages of python for artificial intelligence applications: (Recommended learning: Python video tutorial)

1: The core algorithm of artificial intelligence is completely dependent on C/C, and Python has historically been an important tool for scientific computing and data analysis. Although Python is a scripting language, it has quickly become a tool for scientists because it is easy to learn (MATLAB and others can also do scientific calculations, but the software requires money and is very expensive). As a result, a large number of tool libraries and architectures have been accumulated. Artificial intelligence involves a large number of Data calculation is natural, simple and efficient using Python.

2: Although Python is slow, it only calls the AI ​​interface. The real calculations are all the underlying data written in C/C. Using Python, you just write the corresponding logic and it comes out in a few lines of code. If you switch to C, not only will the amount of code be too large, but the development efficiency will be too low. It doesn't mean that you can't write the upper-level logic in C, but it will increase the overall speed by 1%, which is not worth the loss.

3: While Python has concise syntax and rich ecological environment to improve development speed, it also has good support for C. Python combines the advantages of the language and makes up for it through its high compatibility with C. The disadvantage of slow speed is naturally favored by data science researchers and machine learning programmers.

Advantages of python extended language:

For general AI:

1.AIMA - Python implements Russell and Norvig's 'Artificial Intelligence: A Modern Approach' library.

2.pyDatalog - Logic programming engine SimpleAI in Python - Python implements many artificial intelligence algorithms described in the book "AIMA". It focuses on providing an easy-to-use, well-documented testing library.

3.EasyAI - a simple Python engine for AI two-player games, such as Negamax, transposition tables, game solving.

For machine learning:

1.PyBrain - Flexible, simple, but very efficient for machine algorithm tasks, it is a machine learning modular library for Python. It also provides various predefined environments to test and compare your algorithms.

2.PyML - A bilateral framework written in Python focusing on SVM and other kernel methods. It supports running on Linux and Mac OS X.

3.scikit-learn - Designed to provide simple yet powerful solutions that are reusable in a variety of contexts: Machine learning as a versatile tool for science and engineering. It is a Python module that integrates classic machine learning algorithms in Python packages that are closely integrated with the scientific world (such as numpy, scipy, matplotlib).

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