Research on the Construction System of Language Service Platform based on Computer Corpus
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The language service platform is an online platform that provides real-time language services for enterprises and individuals. The demanders can obtain the required language services quickly and find professional language services immediately, saving time and effort. A language service platform was built based on client/server. The platform includes online, photo, manual translation and language selection. The service platform builds a corpus, provides standardized services and realizes applications with multiple interaction methods for multi-platform devices. The online translation uses Python to crawl data and implements corpus services through the. NET platform; manual translation builds an artificial translation module through the. NET platform and uses WebAPI to provide external services. The language service platform is part of the national language capacity, essential in promoting international economic and cultural exchanges.Keywords:
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This chapter shows you how to use Python in Azure Machine Learning (Azure ML). Using simple examples, you will learn how to integrate Python as part of an Azure ML experiment. This enables you to tap into the powerful capabilities offered by various Python libraries, such as NumPy, SciPy, pandas, scikit-learn, and many more, directly in an Azure ML experiment.
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This technical note introduces the Python bindings for libcloudph++. The libcloudph++ is a C++ library of algorithms for representing atmospheric cloud microphysics in numerical models. The bindings expose the complete functionality of the library to the Python users. The bindings are implemented using the Boost.Python C++ library and use NumPy arrays. This note includes listings with Python scripts exemplifying the use of selected library components. An example solution for using the Python bindings to access libcloudph++ from Fortran is presented.
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We present $\textbf{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\textbf{PyRMLE}$ is simple to use and readily works with data formats that are typical to Random Coefficient problems. The module makes use of Python's scientific libraries $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features.
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NumPy, or Numerical Python, is a Python-based library for mathematical computations and processing arrays. Python does not support data structures in more than one dimension, with containers like lists, tuples, and dictionaries being unidimensional. The inbuilt data types and containers in Python cannot be restructured into more than one dimension, and also do not lend themselves to complex computations. These drawbacks are limitations for some of the tasks involved while analyzing data and building models, which makes arrays a vital data structure.
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