Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
This work aims to provide profound insights into neural networks and deep learning, focusing on their functioning, interpretability, and generalization capabilities. It explores fundamental aspects such as network architectures, activation functions, and learning algorithms, analyzing their theoreti...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38934/1/Theoretical_Insights_into_Neural_Networks_and_Deep_Learning_Advancing_Understanding_Interpretability_and_Generalization.pdf http://umpir.ump.edu.my/id/eprint/38934/2/Theoretical%20Insights%20into%20Neural%20Networks%20and%20Deep%20Learning.pdf http://umpir.ump.edu.my/id/eprint/38934/ https://doi.org/10.1109/WCONF58270.2023.10235042 |
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