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Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

Author

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  • Namatēvs Ivars

    (Riga Technical University, Riga, Latvia)

Abstract

Deep convolutional neural networks (CNNs) are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

Suggested Citation

  • Namatēvs Ivars, 2017. "Deep Convolutional Neural Networks: Structure, Feature Extraction and Training," Information Technology and Management Science, Sciendo, vol. 20(1), pages 40-47, December.
    • Handle: RePEc:vrs:itmasc:v:20:y:2017:i:1:p:40-47:n:7
    DOI: 10.1515/itms-2017-0007
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