Mobile Internet takes the lead in the forefront of the Internet era instead of PC Internet. Android and iOS once became the two overlords of mobile Internet application platforms and became the first two technologies for mobile developers. HTML5 occupies an important position in mobile Internet application platforms with its cross-platform advantages, which can be said to be the home of the latecomers.Due to technical constraints, it is difficult to generate more new applications. Internet plus's products are increasingly saturated. Mobile Internet is gradually developing smoothly from The Peak Time. Who will be the main stadium in the next era?Who will be in charge of the next application technology?
In the 3rd Internet Conference, Baidu CEO Li Yanhong once stated: It is impossible to see a unicorn again through the tuyere of mobile Internet, because the market has entered a relatively stable development stage and the Internet population penetration rate has exceeded 50%.The future opportunity lies in artificial intelligence.It is true that Internet giants have significantly increased their investment in the field of artificial intelligence and are striving to be the "leading eldest brother" in the era of artificial intelligence.
Python, as a programming language, is far more attractive than C#, Java,C,C++. It is nicknamed "glue language" and is praised as "the most beautiful" programming language by programmers who love it.Python applications are ubiquitous from cloud, client and Internet of Things terminal, and it is also the first programming language of artificial intelligence.
Advantages of Python Programming Language in Artificial Intelligence
1. Quality Documents
2. Platform independent, can be used on every *nix version now.
3. Compared with other Object Oriented Programming, learning is simpler and faster
4.Python has many image enhancement libraries such as Python Imaging Libary,VTK and Maya 3D The Eclipse Visualization Toolkit, Numeric Python, Scientific Python and many other available tools can be used for numerical and scientific applications.
5.Python's design is very good, fast, robust, portable and extensible.Obviously these are very important factors for Applications of artificial intelligence.
6. Useful for a wide range of programming tasks for scientific purposes, from small shell scripts to entire web applications.
7. Finally, it is open source.Can get the same community support.
Python library for AI
overall AI library
AIMA：Python Implements "Artificial Intelligence: A Modern Method" Algorithm from Russell to Norvigs
Py Datalog: Logic programming Engine in Python
SimpleAI：Python implements the algorithm of artificial intelligence described in the book "artificial intelligence: a modern method".It focuses on providing an easy-to-use library with good documentation and testing.
EasyAI: python Engine for a Double AI Game (Negative Maximum, Translation Table, Game Resolution)
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 predefined environments to test and compare your algorithms.
PyML is a bilateral framework written in Python, focusing 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 is a multifunctional tool for science and engineering.It is a module of python and integrates classical machine learning algorithms. These algorithms are closely linked with python Science Package (numpy,scipy.matplotlib).
MDP-Toolkit This is a Python data processing framework that can be easily extended.It collects supervised and unsupervised learning algorithms and other data processing units, which 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 pattern learning methods (local linear embedding), centralized classification, probability methods (factor analysis, RBM), data preprocessing methods, etc.
Natural Language and Text Processing Library
natural language toolkit's open source Python module, linguistic data and documents, is used to research and develop natural language processing and text analysis.Windows,Mac OSX and Linux versions are available.