The functions and advantages of the Python language in artificial intelligence
The Python language is an object-oriented, literal translation computer programming language. The Python syntax is simple, clear, and easy to read. Python is an open source language with a rich and powerful class library, excellent extensibility and platform portability, and it's easy to link modules made in other languages. This article mainly explains the functions and advantages of the Python language in artificial intelligence, and follows the small series to learn more about it in detail. Due to its simplicity and high development efficiency, Python has been favored by more and more companies. Artificial intelligence has become the hottest topic in the moment. The future can be said to be the world of artificial intelligence. The company chose Python for Web development, search, Google, OpenStack, big data, artificial intelligence, scientific computing and other directions. Python will become the third mainstream programming language after C++ and Java. Python's artificial intelligence is also the advantage course of Dana. The employment advantage of Python is also obvious. Nowadays, mobile Internet has replaced the PC Internet as the forefront of the Internet era. Android and iOS have become the two dominant players in the mobile Internet application platform, becoming the preferred technology for mobile developers. HTML5 is based on its cross-platform advantages in mobile Internet applications. The platform occupies an important position, which can be said to be the latecomer. Due to the limitations of technology, it is difficult to generate more new applications. Internet+ products are becoming more and more saturated. The mobile Internet has gradually developed from the peak era. Who is the home of the next era? Who is the next application technology? In the third Internet conference, Baidu CEO Li Yanhong once said: It is no longer possible to have a unicorn with the wind C1 of the mobile Internet, because the market has entered a relatively stable development stage, and the Internet population penetration rate has exceeded 50%. . And the opportunities for the future are in artificial intelligence. It is true that Internet giant companies have significantly increased their investment in artificial intelligence, and they are striving to be the "big brothers" in the era of artificial intelligence. As a programming language, Python is far more attractive than C#, Java, C, C++. It is nicknamed "glue language" and is the most beautiful "programming language" for programmers who love it. From the cloud, client, To the Internet of Things terminal, Python applications are ubiquitous and the first programming language for artificial intelligence. 1. More humanized design Python's design is more user-friendly, fast, sturdy, portable, and extensible, very suitable for artificial intelligence; open source is free, and easy to learn, easy to achieve universal; 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: The logic programming engine in Python; SimpleAI: Python implements the artificial intelligence algorithm described in the book "Artificial Intelligence: A Modern Approach", which focuses on providing an easy-to-use, well-documented and tested library; EasyAI: A python engine for double AI games. 3, machine learning library PyBrain A flexible, simple and effective algorithm for machine learning tasks. It is a modular Python machine learning library that also provides a variety of predefined environments to test and compare your algorithms. PyML A bilateral framework written in Python that focuses on SVM and other kernel methods, and supports Linux and Mac OS X; Scikit-learn is designed to provide a simple yet powerful solution that can be reused in different contexts: machine learning is a versatile tool for science and engineering. It is a module of Python that integrates classic machine learning algorithms. The algorithm is closely tied to the python science package; MDP-Toolkit This is a framework for Python data processing that can be easily extended. It collects supervised and unregulated learning meals 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 steadily, including signal processing methods, stream learning methods, centralized classification, probabilistic methods, data preprocessing methods, and so on. 4, natural language and text processing library 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 makes it easy to link modules made in other languages, so 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 grasp the hurdles of artificial intelligence, Python is an essential boost. 1, quality documents 2, platform-independent, can be used on every *nix version now 3, and other object-oriented programming languages ​​are easier and faster to learn than 4. Python has many image enhancement libraries like Python Imaging Libary, VTK and Maya 3D Visualization Toolkit, Numeric Python, ScienTIfic Python and many other tools available for numerical and scientific applications. 5, Python design is very good, fast, sturdy, portable, and scalable. Obviously these are very important factors for artificial intelligence applications. 6. Useful for a wide range of programming tasks for scientific purposes, from small shell scripts to full-site 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 the artificial intelligence algorithm described in the book "Artificial Intelligence: A Modern Approach." It focuses on providing an easy to use, well documented and tested library. EasyAI: A python engine for double AI games (negative value, replacement 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 is designed to provide a simple yet powerful solution 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 tied to the python science package (numpy, scipy.matplotlib). MDP-Toolkit This is a framework for Python data processing that can be easily extended. It collects supervised and unregulated learning meals 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 steadily, including signal processing methods (principal component analysis, independent component analysis, slow feature analysis), flow learning methods (local linear embedding), centralized classification, probabilistic methods (factor analysis, RBM). , data preprocessing methods, and more. 1, LISP High-level languages ​​like LISP are favored in artificial intelligence because they have chosen rapid prototyping after years of research in colleges and universities and abandoned rapid execution. 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 LISP high-level and traditional advantages, which is very useful for AI. Its advantage is to solve "logic-based problems." Prolog provides solutions to logic-related problems, or its solutions have simple logical features. Its main drawback (IMHO) is hard to learn. 3, C / C + + Like cheetahs, C/C++ is primarily used when execution speed is critical. 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 improve a little bit faster. 4, JAVA Newcomers, Java uses several concepts in LISP, the most obvious being garbage collection. Its portability makes it suitable for any program, and it has a built-in type. Java doesn't have LISP and Prolog advanced, and it's not as fast as C, but it's best if portability is required.
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