ZotWeb	article-journal
Src Url	Pouyanfar, Sadiq, Yan, Tian, Tao, Reyes, Shyu, Chen, Iyengar (2018)

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

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Citer: (Pouyanfar et al., 2018)

FTag: Pouyanfar-et-al-2018

APA7: Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S. S. (2018). A Survey on Deep Learning: Algorithms, Techniques, and Applications. ACM Computing Surveys, 51(5), 92:1–92:36. https://doi.org/10.1145/3234150

Deep learning uses multiple layers to represent the abstractions of data to build computational models.
DL | AIDef | DeepLearning | ArticleAkten

[...] “deep learning,” also known as representation learning [ 29 ]

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[29] Li Deng. 2014. A tutorial survey of architectures, algorithms, and applications for deep learning.APSIPA Transactionson Signal and Information Processing 3 (2014), 1–29.
DL | ArticleAkten

In recent years, machine learning has become more and more popular in research and has been incorporated in a large number of applications, including multimedia concept retrieval, image clas- sification, video recommendation, social network analysis, text mining, and so forth.
AIContext

Deep learning, which has its roots from conventional neural networks, significantly outperforms its predecessors. It utilizes graph technologies with transformations among neurons to develop many-layered learning models.

#concept
AIConcept | DLConcept | ref2012012000

[...] deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing.  (Pouyanfar et al., 2018, p.1)
AIEtat

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