Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks

Journal article

Nazir, S., Patel, S. and Patel, D. (2020). Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks. International Journal of Artificial Intelligence and Machine Learning. 10 (2), pp. 1-17. https://doi.org/10.4018/ijaiml.2020070101
AuthorsNazir, S., Patel, S. and Patel, D.
AbstractThe increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures.
Keywordsanomaly detection; SCADA; clustering; classification; IoT; neural networks; intrusion detection; machine learning; autoencoders
Year2020
JournalInternational Journal of Artificial Intelligence and Machine Learning
Journal citation10 (2), pp. 1-17
PublisherIGI Global
ISSN2642-1577
2642-1585
Digital Object Identifier (DOI)https://doi.org/10.4018/ijaiml.2020070101
Publication dates
Print16 Jul 2020
Publication process dates
Accepted09 Jun 2020
Deposited20 Jun 2020
Accepted author manuscript
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Open
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Autoencoder based Anomaly Detection for SCADA Networks.pdf
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