Review and comparative analysis of machine learning libraries for machine learning

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 th...

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Published in:Discrete and continuous models and applied computational science Vol. 27; no. 4; pp. 305 - 315
Main Authors: Gevorkyan, Migran N., Demidova, Anastasia V., Demidova, Tatiana S., Sobolev, Anton A.
Format: Journal Article
Language:English
Published: Peoples’ Friendship University of Russia (RUDN University) 15.12.2019
Subjects:
machine learning
mnist
neural networks
pytorch
tensorflow
ISSN:2658-4670, 2658-7149
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Gevorkyan, Migran N.
Demidova, Tatiana S.
Demidova, Anastasia V.
Sobolev, Anton A.
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SubjectTerms machine learning
mnist
neural networks
pytorch
tensorflow
Title Review and comparative analysis of machine learning libraries for machine learning
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