IEEE Access (Jan 2020)

IEEE Access Special Section Editorial: Scalable Deep Learning for Big Data

  • Liangxiu Han,
  • Daoqiang Zhang,
  • Omer Rana,
  • Yi Pan,
  • Sohail Jabbar,
  • Mazin Yousif,
  • Moayad Aloqaily

DOI
https://doi.org/10.1109/ACCESS.2020.3041166
Journal volume & issue
Vol. 8
pp. 216617 – 216622

Abstract

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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).