eprintid: 11405
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/14/05
datestamp: 2023-11-10 03:25:55
lastmod: 2023-11-10 03:25:55
status_changed: 2023-11-10 01:15:11
type: article
metadata_visibility: show
creators_name: Khan, U.
creators_name: Khan, K.
creators_name: Hasssan, F.
creators_name: Siddiqui, A.
creators_name: Afaq, M.
title: Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines
ispublished: pub
note: cited By 8
abstract: Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines. © 2019, Dr D. Pylarinos. All rights reserved.
date: 2019
publisher: Dr D. Pylarinos
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156591632&doi=10.48084%2fetasr.2734&partnerID=40&md5=697899b4f0692380d6ffb9fc9a4b7f05
id_number: 10.48084/etasr.2734
full_text_status: none
publication: Engineering, Technology and Applied Science Research
volume: 9
number: 4
pagerange: 4423-4427
refereed: TRUE
issn: 22414487
citation:   Khan, U. and Khan, K. and Hasssan, F. and Siddiqui, A. and Afaq, M.  (2019) Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines.  Engineering, Technology and Applied Science Research, 9 (4).  pp. 4423-4427.  ISSN 22414487