A modified Adam algorithm for deep neural network optimization

Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained to learn the relationships between various va...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 23; S. 17095 - 17112
Hauptverfasser:	Reyad, Mohamed, Sarhan, Amany M., Arafa, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2023 Springer Nature B.V
Schlagworte:	Accuracy Adaptive algorithms Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convergence Data Mining and Knowledge Discovery Datasets Image Processing and Computer Vision Machine learning Mathematical models Neural networks Optimization Original Article Parameter modification Performance evaluation Probability and Statistics in Computer Science Training Adam RMSprop Optimizer AMSGrad Nesterov Accelerated Adam Deep Neural Networks Adaptive Moment Estimation
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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