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Review of Deep Learning

YIN, Bao-cai ; WANG, Wen-tong ; WANG, Li-chun

Bĕijīng gōngyè dàxúe xúebào, 2015-01, Vol.41 (1), p.48-59

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Título:
Review of Deep Learning
Autor: YIN, Bao-cai ; WANG, Wen-tong ; WANG, Li-chun
Assuntos: Algorithms ; Architecture (computers) ; Balances (scales) ; Learning ; Networks ; State of the art ; Streams ; Training
É parte de: Bĕijīng gōngyè dàxúe xúebào, 2015-01, Vol.41 (1), p.48-59
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: Considering deep learning's importance in academic research and industry application, this paper reviews methods and applications of deep learning. First, the concept of deep learning is introduced, and the main stream deep learning algorithms are classified into three classes; feed-forward deep networks, feed-back deep networks and bi-directional deep networks according to the architectural characteristics. Second, network architectures and training methods of the three types of deep networks are reviewed. Finally, state-of-the-art applications of mainstream deep learning algorithms is illustrated and trends of deep learning is concluded. Although deep learning algorithms outperform traditional methods in many fields, there are still many issues, such as feature learning on unlabeled data; the balance among network scale, training speed and accuracy; and model fusion.
Idioma: Chinês

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