Yuriy Kochura, Sergii Stirenko, Anis Rojbi, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko
Published 2017-06-07Version 1
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.
Comments: 4 pages, 6 figures, 4 tables; XIIth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2017), Lviv, Ukraine
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
![QR code for arXiv:1706.02248 [cs.LG]](http://oss.arxitics.com/images/favicon.svg)