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Python and artificial intelligence
Python and artificial intelligence
Google's AI beat a weiqi master, a quick way to weigh the sudden intelligence of artificial intelligence, and to remind them how and how these technologies can be carried out in the future.
AI is a future technology, and now is trying to discuss a set of tools. A series of pause has occurred in the past few years: an accident free driving over 300000 miles and a milestone for automatic driving in three states; IBM Waston defeated the two time champion of Jeopardy; statistical learning technology is identified from a consumer interest to a complex data set of trillions of images. These developments inevitably advance the interest of scientists and great masters in AI, which makes developers understand the real essence of AI applications. The first thing to develop these needs is:
Which programming language is suitable for artificial intelligence?
Every programming language you control well can be the development language of AI.
AI programs can use almost all programming languages to finish. The most common are Lisp, Prolog, C/C++, Java recently, and Python. recently.
LISP
Advanced languages like LISP are very popular in AI. Due to the rapid prototyping after years of research, colleges and universities have abandoned the fast implementation. Dregs collection, dynamic types, data functions, unified syntax, interactive environment and extensibility make LIST very suitable for artificial intelligence programming.
PROLOG
This kind of speech is effectively separated from LISP high-level and traditional advantages, which is very useful for AI. Its advantage is to deal with logic based problems. Prolog provides a processing plan for logic related problems, or its processing plan has simple logical characteristics. Its main defect is to learn very hard.
C/C++
Like cheetahs, C/C++ is mainly used for high execution speed requests. It is mainly used for simple programs, statistical artificial intelligence, such as neural network is a common example. Backpropagation uses only a few pages of C/C++ code, but the speed of request is even better if the programmer can only improve a little bit of speed.
JAVA
For the newcomers, Java has applied several ideas in LISP, and the most obvious thing is the collection of trash. Its portability enables it to be applied to any program, and it has a built-in type. Java doesn't have LISP and Prolog advanced, and is not as fast as C, but if request portability, it's the best.
PYTHON
Python is a language compiled with LISP and JAVA. According to Norvig's comparison of Lips and Python, these two languages are very similar to each other, with only minor differences. JPthon also provides access to Java image user interface. This is the reason why PeterNorvig chose to translate programs in JPyhton books by others. JPython enables him to use portable GUI demonstrations and portable http/ftp/html libraries. Therefore, it is quite suitable for artificial intelligence speech.
The advantage of using Python in AI is better than other programming languages.
High quality documents
Platform independent, which can be applied to every *nix version now.
Compared with other object-oriented programming languages, learning is simpler and faster.
Python has many image enhancement libraries like Python Imaging Libary, VTK and Maya 3D visualization toolkits, Numeric Python, Scientific Python, and many other available tools that can be used in numerical and scientific applications.
The design of Python is excellent, fast, consolidated, portable and extensible. Obviously, these are very important elements in AI application.
The universal programming task of scientific use is very useful, no matter from the shell script or from the whole website application.
Finally, it's open source. We can get the same community support.
The Python Library of AI
The overall AI Library
AIMA:Python has completed the algorithm of AI: a modern way from Russell to Norvigs.
Logic programming engine in pyDatalog:Python
SimpleAI:Python completed the algorithm of artificial intelligence described in the book "Ai: a modern way". It focuses on providing a library that is easy to use, well documented and tested.
EasyAI: the python engine of a two person AI game (negative big value, replacement table, game processing).
Machine learning library
PyBrain is a sensitive, simple and effective algorithm for machine learning tasks. It is a modular Python machine learning library. It also provides a variety of predefined environments to test and compare your algorithm.
PyML, a bilateral framework written by Python, focuses on SVM and other kernel approaches. It supports Linux and Mac OS X.
Scikit-learn is designed to provide a simple and powerful processing plan 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 that are closely linked to the python numpy (scipy.matplotlib).
MDP-Toolkit, which is a framework for Python data disposal, can easily stop expanding. The sea has collected supervised and unsupervised learning calculations and other data disposal units, which can be combined into data disposal sequences or more complex feedforward networks. The completion of the new algorithm is simple and intuitive. The available algorithms are increased from time to time, including signal disposal methods (principal component analysis, independent component analysis, slow feature analysis), flow pattern learning (partial linear embedding), centralized classification, probability method (factor profile)
AI is a future technology, and now is trying to discuss a set of tools. A series of pause has occurred in the past few years: an accident free driving over 300000 miles and a milestone for automatic driving in three states; IBM Waston defeated the two time champion of Jeopardy; statistical learning technology is identified from a consumer interest to a complex data set of trillions of images. These developments inevitably advance the interest of scientists and great masters in AI, which makes developers understand the real essence of AI applications. The first thing to develop these needs is:
Which programming language is suitable for artificial intelligence?
Every programming language you control well can be the development language of AI.
AI programs can use almost all programming languages to finish. The most common are Lisp, Prolog, C/C++, Java recently, and Python. recently.
LISP
Advanced languages like LISP are very popular in AI. Due to the rapid prototyping after years of research, colleges and universities have abandoned the fast implementation. Dregs collection, dynamic types, data functions, unified syntax, interactive environment and extensibility make LIST very suitable for artificial intelligence programming.
PROLOG
This kind of speech is effectively separated from LISP high-level and traditional advantages, which is very useful for AI. Its advantage is to deal with logic based problems. Prolog provides a processing plan for logic related problems, or its processing plan has simple logical characteristics. Its main defect is to learn very hard.
C/C++
Like cheetahs, C/C++ is mainly used for high execution speed requests. It is mainly used for simple programs, statistical artificial intelligence, such as neural network is a common example. Backpropagation uses only a few pages of C/C++ code, but the speed of request is even better if the programmer can only improve a little bit of speed.
JAVA
For the newcomers, Java has applied several ideas in LISP, and the most obvious thing is the collection of trash. Its portability enables it to be applied to any program, and it has a built-in type. Java doesn't have LISP and Prolog advanced, and is not as fast as C, but if request portability, it's the best.
PYTHON
Python is a language compiled with LISP and JAVA. According to Norvig's comparison of Lips and Python, these two languages are very similar to each other, with only minor differences. JPthon also provides access to Java image user interface. This is the reason why PeterNorvig chose to translate programs in JPyhton books by others. JPython enables him to use portable GUI demonstrations and portable http/ftp/html libraries. Therefore, it is quite suitable for artificial intelligence speech.
The advantage of using Python in AI is better than other programming languages.
High quality documents
Platform independent, which can be applied to every *nix version now.
Compared with other object-oriented programming languages, learning is simpler and faster.
Python has many image enhancement libraries like Python Imaging Libary, VTK and Maya 3D visualization toolkits, Numeric Python, Scientific Python, and many other available tools that can be used in numerical and scientific applications.
The design of Python is excellent, fast, consolidated, portable and extensible. Obviously, these are very important elements in AI application.
The universal programming task of scientific use is very useful, no matter from the shell script or from the whole website application.
Finally, it's open source. We can get the same community support.
The Python Library of AI
The overall AI Library
AIMA:Python has completed the algorithm of AI: a modern way from Russell to Norvigs.
Logic programming engine in pyDatalog:Python
SimpleAI:Python completed the algorithm of artificial intelligence described in the book "Ai: a modern way". It focuses on providing a library that is easy to use, well documented and tested.
EasyAI: the python engine of a two person AI game (negative big value, replacement table, game processing).
Machine learning library
PyBrain is a sensitive, simple and effective algorithm for machine learning tasks. It is a modular Python machine learning library. It also provides a variety of predefined environments to test and compare your algorithm.
PyML, a bilateral framework written by Python, focuses on SVM and other kernel approaches. It supports Linux and Mac OS X.
Scikit-learn is designed to provide a simple and powerful processing plan 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 that are closely linked to the python numpy (scipy.matplotlib).
MDP-Toolkit, which is a framework for Python data disposal, can easily stop expanding. The sea has collected supervised and unsupervised learning calculations and other data disposal units, which can be combined into data disposal sequences or more complex feedforward networks. The completion of the new algorithm is simple and intuitive. The available algorithms are increased from time to time, including signal disposal methods (principal component analysis, independent component analysis, slow feature analysis), flow pattern learning (partial linear embedding), centralized classification, probability method (factor profile)