Python, an object-oriented, interpreted computer programming language, was invented in 1989 by the Dutchman Guido van Rossum, and the first public release was published in 1991.
Since the birth of the Python language in the early 1990s, it has been widely used in the processing of system management tasks and Web programming. In January 2011, it was named the 2010 language by the TIOBE programming language rankings. Since 2004, Python usage has grown linearly. On July 20 last year, IEEE released the 2017 programming language rankings: Python topped the list.
What makes Python the most popular machine learning language today! Is it the context of the big data era, or is it unique to the ecosystem, or the language itself?
First of all, of course, because everyone's life is very short, Python as an interpreted language, although running slower than anyone else, but reduced the developer's workload. The philosophy of Python developers is "use one method, preferably there is only one way to do one thing." When designing the Python language, if faced with multiple choices, Python developers generally reject fancy grammars and choose explicit grammars with little or no ambiguity. The easy-to-learn features have spawned a large user community and active community. The makers of machine learning frameworks are also used to get more people to use the mass line. Python is more grounded.
The second main reason why Python has become the main force of machine learning is because Python provides a large number of machine learning code libraries and frameworks. With Python, you can enjoy many convenient third-party libraries of mathematical operations, such as NumPy and SciPy. There are MatplotLib and SeaBorn in visualization. Structured data operations can get R general experience through Pandas, for various vertical fields such as image, voice and text. In the pre-processing stage, there are very mature libraries that can be called. People often say: "On your library." The Python standard library is really huge. It can help with a variety of tasks, including regular expressions, document generation, unit testing, threads, databases, web browsers, CGI, FTP, email, XML, XML-RPC, HTML, WAV files, password systems, GUI, Tk and other system related operations. This is called Python's "full-featured" philosophy. In addition to the standard library, there are many other high-quality libraries, such as wxPython, Twisted, and Python image libraries.
Of course, this code system has some shortcomings, so there are a lot of workarounds. For example, distributions such as Anaconda have their own packaging mechanism that handles dependencies on executables that are not part of the Python ecosystem. However, in general, the Python packaging ecosystem provides a degree of convenience for machine learning, which is consistent with Python's consistent simplicity and convenience.
Finally, it is the performance aspect. Of course, Python performance can't satisfy large-scale data training, so the average enterprise first builds the prototype with Python, then uses C++ or JAVA to realize the engineering, and then uses Python to encapsulate the interface. In addition, thanks to Python's interface to C, many efficient, Python-friendly libraries like gnumpy, theano can speed up the running of programs. With the support of a powerful team, these libraries may be more efficient than an unskilled programmer. C is more efficient in writing one month of tuning.
So, let Python stand out in the field of machine learning not just a single function, but Python's entire language pack: easy to learn to make it more grounded, its ecosystem has a third-party code base that can cover a wide range of machine learning use cases. And performance can help you get the job done right.
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