Deep learning for engineers / Tariq M. Arif (2024)  

Public ISBD Titre : Deep learning for engineers Type de document : document électronique Auteurs : Tariq M. Arif, Auteur ; Md Adilur Rahim, Auteur Editeur : London ; New York ; Boca Raton : CRC Press Année de publication : 2024 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-1-00-340292-3 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Engineering--Study and teaching Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful. Note de contenu : Summary : 1. Basics of deep learning. 2. Computer vision fundamentals. 3. Natural language processing fundamentals. 4. Deep learning framework installation: pytorch and cuda. 5. Case study i: image classification. 6. Case study ii: object detection. 7. Case study iii: semantic segmentation. 8. Case study iv: image captioning. En ligne : https://research.ebsco.com/linkprocessor/plink?id=a4b6d807-80a1-33ec-be95-88f82d [...] Deep learning for engineers [document électronique] / Tariq M. Arif, Auteur ; Md Adilur Rahim, Auteur . - London ; New York ; Boca Raton : CRC Press, 2024 . - 1 fichier PDF : ill. ISBN : 978-1-00-340292-3 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Engineering--Study and teaching Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful. Note de contenu : Summary : 1. Basics of deep learning. 2. Computer vision fundamentals. 3. Natural language processing fundamentals. 4. Deep learning framework installation: pytorch and cuda. 5. Case study i: image classification. 6. Case study ii: object detection. 7. Case study iii: semantic segmentation. 8. Case study iv: image captioning. En ligne : https://research.ebsco.com/linkprocessor/plink?id=a4b6d807-80a1-33ec-be95-88f82d [...]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
E00416	004.8 ARI	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Téléchargeable