Neural network with deep learning architectures

被引：27

作者：

Patel, Hima ^{[1
]}

Thakkar, Amit ^{[1
]}

Pandya, Mrudang ^{[1
]}

Makwana, Kamlesh ^{[1
]}

机构：

[1] Charotar Univ Sci & Technol, Changa 388421, Gujarat, India

来源：

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES | 2018年 / 39卷 / 01期

关键词：

Neural Network; Deep Learning; Deep Neural Network; Stacked Autoencoder; Convolution Neural Network; Recurrent Neural Network;

D O I：

10.1080/02522667.2017.1372908

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

Deep Learning is a field included in to Artificial Intelligence. It allows computational models to learn multiple levels of abstraction with multiple processing layers. This Artificial Neural Networks gives state-of-art performance in various fields like Computer Vision, Speech recognition and different domain like bioinformatics. There are mainly three architectures of Deep Learning Convolution Neural Network, Deep Neural Network and Recurrent Neural Network which provides the higher level of representation of data at each next layer. Deep Learning is required to classify high dimensional data like images, audio, video and biological data.

引用

页码：31 / 38

页数：8

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