Neural network with deep learning architectures. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Patel, Hima; Thakkar, Amit; Pandya, Mrudang; Makwana, Kamlesh
• Is Part Of: Journal of information & optimization sciences. Volume 39:Issue 1(2018)
• Page Start: 31
• Page End: 38
• Publication Date: 2018-01-02
• Subjects: Neural Network -- Deep Learning -- Deep Neural Network -- Stacked Autoencoder -- Convolution Neural Network -- Recurrent Neural Network; 92B20; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2017.1372908
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: 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.