IEEE Access special section editorial: scalable deep learning for big data

Han, Liangxiu, Zhang, Daoqiang, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Pan, Yi, Jabbar, Sohail, Yousif, Mazin and Aloqaily, Moayad 2020. IEEE Access special section editorial: scalable deep learning for big data. IEEE Access 8 , 216617 - 216622. 10.1109/ACCESS.2020.3041166

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Deep learning (DL) has emerged as a key application exploiting the increasing computational power in systems such as GPUs, multicore processors, Systems-on-Chip (SoC), and distributed clusters. It has also attracted much attention in discovering correlation patterns in data in an unsupervised manner and has been applied in various domains including speech recognition, image classification, natural language processing, and computer vision. Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. In addition, now, a number of commercial vendors also offer accelerators for deep learning systems (such as Nvidia, Intel, and Huawei).

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	This work is licensed under a Creative Commons Attribution 4.0 License
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	2169-3536
Date of First Compliant Deposit:	21 December 2020
Date of Acceptance:	14 December 2020
Last Modified:	03 May 2023 02:04
URI:	https://orca.cardiff.ac.uk/id/eprint/137136

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item