Work

Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes

Year: 2017

Type: conference-paper

Abstract: The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.

Source: International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)

DOI: 10.1109/stc-csit.2017.8098808

Authors: Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

Institution: National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2623992906

FWCI: 3.682

Cites: 9

Cited by: 28

Related to: 10