The Python language is an object-oriented, transliterated computer programming language. Python syntax is simple, clear, and easy to read. Python is an open source language with a rich and powerful class library. It also has excellent extensibility and platform portability. It can easily link various modules made in other languages ​​together. This article mainly explains the function and advantage of Python language in artificial intelligence, and specifically follows the small series to learn more about it.
Why use Python to develop artificial intelligenceBecause of its concise and beautiful and high development efficiency, Python has been favored by more and more companies. Artificial intelligence has become the most popular topic nowadays. The future can be said to be the world of artificial intelligence.
The company chose Python for Web site development, search for Google, cloud computing, OpenStack, big data, artificial intelligence, and scientific computing.
Python will become the third mainstream programming language following C++ and Java. Python will also be an advantage course for Dane. Python's talent employment advantage is also obvious.
Nowadays, mobile Internet takes the lead in the era of Internet in place of PC Internet, Android and iOS- become two dominant players in the mobile Internet application platform, and become the preferred two technologies for mobile developers. HTML5 takes advantage of its cross-platform advantages in mobile Internet applications. The platform occupies an important position and it can be said that the latecomers are on the top. Due to technical limitations, it is difficult to generate more new applications. Internet+ products are becoming more and more saturated. Mobile Internet gradually tends to develop smoothly from the peak age. Who is the home in the next era? Who will be the next application technology?
In the third session of the Internet Conference, Baidu CEO Li Yanhong once stated that it is no longer possible to rely on mobile Internet C1 to appear as a unicorn, because the market has entered a relatively stable development stage and the Internet population penetration rate has exceeded 50%. . And the future opportunities in artificial intelligence. It is true that the Internet giants have significantly increased their investment in artificial intelligence and are striving to be the “leader†in the era of artificial intelligence.
As a programming language, Python's charisma is far beyond C#, Java, C, and C++. It is nicknamed "glue language" and it is also loved by programmers as its most beautiful "programming language. From the cloud, the client, To the Internet of Things terminals, Python applications are everywhere and are the first programming language for artificial intelligence.
1, more humane design
Python design is more humane, fast, sturdy, portable, and extensible. It is very suitable for artificial intelligence; open source is free, and learning is simple, it is easy to achieve universality; built-in powerful library, you can easily achieve more powerful The function.
2, the overall AI library
AIMA: Python implements the "artificial intelligence: a modern approach" algorithm from Russell to Norvigs;
pyDatalog: Logic programming engine in Python;
SimpleAI: Python implements artificial intelligence algorithms described in the book "Artificial Intelligence: A Modern Approach", which focuses on providing a library that is easy to use, well documented, and tested;
EasyAI: A double AI game python engine.
3, machine learning library
PyBrain is a flexible, simple and effective algorithm for machine learning tasks. It is a modular Python machine learning library. It also provides a variety of pre-defined environments to test and compare your algorithms.
PyML A bilateral framework written in Python that focuses on SVM and other kernel methods. It supports Linux and Mac OS X;
Scikit-learn is designed to provide simple and powerful solutions that can be reused in different contexts: machine learning as a versatile tool for science and engineering, a module of Python that integrates classic machine learning algorithms. The algorithm is closely linked with the python science package;
MDP-Toolkit This is a Python data processing framework that can be easily extended. It has collected supervised and unregulated learning calculations and other data processing units that can be combined into data processing sequences or more complex feedforward network structures. The implementation of the new algorithm is simple and intuitive. The available algorithms are constantly increasing, including signal processing methods, flow type learning methods, centralized classification, probability methods, data preprocessing methods, and so on.
Natural Language and Text Processing Libraries
NLTK's open source Python module, linguistic data and documentation for research and development of natural language processing and text analysis, available in windows, Mac OSX and Linux.
Python has a rich and powerful library that can easily link various modules made in other languages. Therefore, Python programming is a very useful language for artificial intelligence. It can be said that artificial intelligence and Python are closely linked. If you want to seize the artificial intelligence, Python is an essential help.
1, good documentation
2, platform-independent, can be used on every current *nix version
3, and other object-oriented programming languages ​​easier and faster than learning
Python has many image enhancement libraries like Python Imaging Libary, VTK and Maya 3D Visualization Toolkit, Numeric Python, ScienTIfic Python and many other available tools for numerical and scientific applications.
5, Python's design is very good, fast, sturdy, portable, and extensible. Obviously these are very important factors for artificial intelligence applications.
6. It is useful for a wide range of programming tasks for scientific purposes, from small shell scripts to entire web applications.
7, it is open source. You can get the same community support.
First, the overall AI library
AIMA: Python implements the "Artificial Intelligence: A Modern Approach" algorithm from Russell to Norvigs
pyDatalog: Logic Programming Engine in Python
SimpleAI: Python implements artificial intelligence algorithms described in the book "Artificial Intelligence: A Modern Approach." It focuses on providing an easy to use, well documented and tested library.
EasyAI: A double AI game python engine (negative value, displacement table, game solution)
Second, the machine learning library
PyBrain A flexible, simple and effective algorithm for machine learning tasks. It is a modular Python machine learning library. It also provides a variety of pre-defined environments to test and compare your algorithms.
PyML A bilateral framework written in Python that focuses on SVM and other kernel methods. It supports Linux and Mac OS X.
Scikit-learn aims to provide simple and powerful solutions that can be reused in different contexts: machine learning as a versatile tool for science and engineering. It is a module of Python that integrates classic machine learning algorithms that are closely linked to the python science package (numpy, scipy.matplotlib).
MDP-Toolkit This is a Python data processing framework that can be easily extended. It has collected supervised and unregulated learning calculations and other data processing units that can be combined into data processing sequences or more complex feedforward network structures. The implementation of the new algorithm is simple and intuitive. The available algorithms are steadily increasing, including signal processing methods (principal component analysis, independent component analysis, slow feature analysis), flow type learning methods (local linear embedding), centralized classification, probability methods (factor analysis, RBM) , data preprocessing methods, etc.
1, LISP
High-level languages ​​such as LISP are favored in artificial intelligence because after years of research at various universities, rapid prototyping was chosen and rapid execution was abandoned. Garbage collection, dynamic typing, data functions, unified syntax, interactive environment, and extensibility make LIST ideal for artificial intelligence programming.
2, PROLOG
This language has an effective combination of high-level LISP and traditional advantages, which is very useful for AI. Its advantage is to solve "logic-based problems." Prolog provides solutions to logically related problems, or its solutions have simple logical features. Its main drawback (IMHO) is that it is hard to learn.
3, C/C++
Like Cheetah, C/C++ is mainly used when the execution speed is very high. It is mainly used for simple programs, statistical artificial intelligence, such as neural network is a common example. BackpropagaTIon only uses a few pages of C/C++ code, but requires speed, even if the programmer can only increase a little speed is also good.
4, JAVA
For newcomers, Java uses several ideas from LISP, most notably garbage collection. Its portability makes it suitable for any program. It also has a set of built-in types. Java does not have LISP and Prolog Advanced, and it is not as fast as C, but it is best if portability is required.
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