University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Auteur Ibtihel Boussahel |
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Titre : Artificial Rabbit Optimization For Tuning Deep Learning Parameters Type de document : texte imprimé Auteurs : Yasmine Tigha ; Ibtihel Boussahel ; Semcheddine,Moussa, Directeur de thèse Editeur : Setif:UFA Année de publication : 2023 Importance : 1 vol (84 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Artificial Rabbit Optimization Genetic Algorithms Particle
Swarm Optimization Grey Wolf Optimization Deep Learning learning rate and dropout rateIndex. décimale : 004 - Informatique Résumé : Deep learning models’ performance heavily relies on selecting appropriate hyperparameters,
such as learning rate, dropout rate , and network architecture.
However, finding the optimal values for these hyperparameters is challenging due
to the lack of fixed rules.
In this thesis, we propose using bio-inspired algorithms, including ARO, PSO,
GA, and GWO, to optimize the hyperparameters of a feed-forward neural network.
The experiments are conducted on the MNIST dataset, commonly used for image
analysis. Comparing the accuracy of models trained with these algorithms, with
and without dropout, reveals that bio-inspired algorithms improve deep learning
model accuracy. Incorporating bio-inspired algorithms in hyperparameter tuning
shows promise for optimizing deep learning models. By drawing inspiration from
nature, these algorithms enhance performance and generalization, demonstrating
their efficacy in deep learning hyperparameter tuning
Côte titre : MAI/0761 En ligne : https://drive.google.com/file/d/1WcfNow4t2G8wXoZTx2oNMTp_XM-CbHG0/view?usp=drive [...] Format de la ressource électronique : Artificial Rabbit Optimization For Tuning Deep Learning Parameters [texte imprimé] / Yasmine Tigha ; Ibtihel Boussahel ; Semcheddine,Moussa, Directeur de thèse . - [S.l.] : Setif:UFA, 2023 . - 1 vol (84 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Artificial Rabbit Optimization Genetic Algorithms Particle
Swarm Optimization Grey Wolf Optimization Deep Learning learning rate and dropout rateIndex. décimale : 004 - Informatique Résumé : Deep learning models’ performance heavily relies on selecting appropriate hyperparameters,
such as learning rate, dropout rate , and network architecture.
However, finding the optimal values for these hyperparameters is challenging due
to the lack of fixed rules.
In this thesis, we propose using bio-inspired algorithms, including ARO, PSO,
GA, and GWO, to optimize the hyperparameters of a feed-forward neural network.
The experiments are conducted on the MNIST dataset, commonly used for image
analysis. Comparing the accuracy of models trained with these algorithms, with
and without dropout, reveals that bio-inspired algorithms improve deep learning
model accuracy. Incorporating bio-inspired algorithms in hyperparameter tuning
shows promise for optimizing deep learning models. By drawing inspiration from
nature, these algorithms enhance performance and generalization, demonstrating
their efficacy in deep learning hyperparameter tuning
Côte titre : MAI/0761 En ligne : https://drive.google.com/file/d/1WcfNow4t2G8wXoZTx2oNMTp_XM-CbHG0/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
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