Review and comparative analysis of machine learning libraries for machine learning

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The article is an overview. We carry out the comparison of actual machine learning libraries that can be used the neural networks development. The first part of the article gives a brief description of TensorFlow, PyTorch, Theano, Keras, SciKit Learn libraries, SciPy library stack. An overview of the scope of these libraries and the main technical characteristics, such as performance, supported programming languages, the current state of development is given. In the second part of the article, a comparison of five libraries is carried out on the example of a multilayer perceptron, which is applied to the problem of handwritten digits recognizing. This problem is well known and well suited for testing different types of neural networks. The study time is compared depending on the number of epochs and the accuracy of the classifier. The results of the comparison are presented in the form of graphs of training time and accuracy depending on the number of epochs and in tabular form.

作者简介

Migran Gevorkyan

Peoples’ Friendship University of Russia

编辑信件的主要联系方式.
Email: gevorkyan-mn@rudn.ru

Candidate of Physical and Mathematical Sciences, assistant professor of Department of Applied Probability and Informatics

6, Miklukho-Maklaya St., Moscow 117198, Russian Federation

Anastasia Demidova

Peoples’ Friendship University of Russia

Email: demidova-av@rudn.ru

Candidate of Physical and Mathematical Sciences, assistant professor of Department of Applied Probability and Informatics

6, Miklukho-Maklaya St., Moscow 117198, Russian Federation

Tatiana Demidova

Peoples’ Friendship University of Russia

Email: 1032152607@pfur.ru

student of Department of Applied Probability and Informatics

6, Miklukho-Maklaya St., Moscow 117198, Russian Federation

Anton Sobolev

Peoples’ Friendship University of Russia

Email: 1032152618@pfur.ru

student of Department of Applied Probability and Informatics

6, Miklukho-Maklaya St., Moscow 117198, Russian Federation

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