IEEE Access Special Section Editorial: Scalable Deep Learning for Big Data
| Autor: | Mazin Yousif, Daoqiang Zhang, Yi Pan, Moayad Aloqaily, Omer Rana, Sohail Jabbar, Liangxiu Han |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Contextual image classification business.industry Computer science Deep learning Big data General Engineering Computer architecture Scalability Special section Key (cryptography) General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 216617-216622 (2020) |
| ISSN: | 2169-3536 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|