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Software Library for Machine Learning Implemented in Go Language

Student: Artem Getmanskiy

Supervisor: Eduard Klyshinskiy

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Control Systems and Data Processing in Engineering (Master)

Final Grade: 9

Year of Graduation: 2019

This paper discusses the feasibility and effectiveness of some machine learning algorithms in the Go programming language. The paper presents speed comparison for solving linear algebra problems in Go, C++, Python languages, implemented using well-known and popular libraries: Gonum, Armadillo, Numpy. Also, speed and results comparison for machine learning algorithms, such as linear regression, PCA, DBSCAN, k-means, logistic regression, was made in Go, C++, Python. All mentioned previously algorithms were implemented in Go. For C++ and Python ready-made libraries mlpack and sklearn, respectively, were used. Finally, the conclusion about the expediency of the development of machine learning algorithm packages in the Go programming language is given. Source code can be found on github: https://github.com/bayesiangopher/bayesiangopher

Full text (added May 24, 2019)

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