Comparison of models of deep convolutional neural networks
- 1 University of Liverpool
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Abstract
Recently, deep learning has gained considerable success and acceptance in a variety of fields, attracting an increasing number of researchers who are delving deeper and gaining a broader perspective on the subject. It provides more sustainability and opportunities to advance the development of society and transform the lives of individuals. Consequently, it is crucial for individuals to understand the neural network development path. This paper provides a concise overview of the structure and components of Convolutional Neural Network, as well as some of the most well-known and influential learning models in the history of its development. Through an analysis of various models of convolutional neural network, the workings of convolutional neural networks were investigated. The paper discovered that the structure of neural networks is becoming deeper and more complex in order to achieve greater efficacy and avoid the overfitting issue. For researchers to enhance and advance neural network performance, there are still numerous parameters and perspectives to improve and advance.
Keywords
convolutional neural network, deep learning, machine learning, AlexNet
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Cite this article
Wang,Z. (2023). Comparison of models of deep convolutional neural networks. Applied and Computational Engineering,16,50-55.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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