Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

Ivars Namatēvs

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Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

机译：深度卷积神经网络：结构，特征提取和训练

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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.

机译：深度卷积神经网络（CNN）旨在处理具有已知网络（如拓扑）的数据。它们被广泛用于识别图像中的对象并诊断时间序列数据以及传感器数据分类中的模式。本文的目的是从卷积运算，典型层和用于训练和学习的基本方法方面介绍深层CNN的理论和实践方面。包括一些实际应用，用于信号和图像分类。最后，本文描述了用于从3D传感器数据中分类关键特征的CNN块结构。

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《Information Technology and Management Science》 |2018年第1期|共8页
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Ivars Namatēvs;
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中图分类管理学;
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Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

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