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Deep learning in Remote sensing
Publicado por
LAP LAMBERT Academic Publishing Nov 2023, 2023
ISBN 10: 620744700X
ISBN 13: 9786207447008
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Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. Nº de ref. del artículo: 9786207447008
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Deep learning in Remote sensing
Publicado por
LAP Lambert Academic Publishing, 2023
ISBN 10: 620744700X
ISBN 13: 9786207447008
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this book, an overview of DL is presented that adopts various perspectives such as state-of-the-arts deep learning techniques, Deep learning approaches, applications. Additionally, the potential problems on deep learning technology. This research present. Nº de ref. del artículo: 1299385381
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Deep learning in Remote sensing
Publicado por
LAP LAMBERT Academic Publishing Nov 2023, 2023
ISBN 10: 620744700X
ISBN 13: 9786207447008
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -In this book, an overview of DL is presented that adopts various perspectives such as state-of-the-arts deep learning techniques, Deep learning approaches, applications. Additionally, the potential problems on deep learning technology. This research presents convolutional neural networks (CNNs) which the most utilized DL network type. A survey of the CNN deep learning architectures that are frequently encountered in the literature, along with their strengths and limitations and describes the development of CNNs architectures together with their main features, e.g., AlexNet, VGG, ResNet, DenseNet, GoogLeNet, Inception: ResNet nd Inception V3/ V4, SegNet, U Net, Point CNN and MASK R-CNN .A detailed study on application of Convolutional Neural Network on the remote sensing to extract features is also explained. Challenges that met CNN were discussed.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch. Nº de ref. del artículo: 9786207447008
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Deep learning in Remote sensing : Convolutional Neural Network (CNNs)
Publicado por
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 620744700X
ISBN 13: 9786207447008
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, an overview of DL is presented that adopts various perspectives such as state-of-the-arts deep learning techniques, Deep learning approaches, applications. Additionally, the potential problems on deep learning technology. This research presents convolutional neural networks (CNNs) which the most utilized DL network type. A survey of the CNN deep learning architectures that are frequently encountered in the literature, along with their strengths and limitations and describes the development of CNNs architectures together with their main features, e.g., AlexNet, VGG, ResNet, DenseNet, GoogLeNet, Inception: ResNet nd Inception V3/ V4, SegNet, U Net, Point CNN and MASK R-CNN .A detailed study on application of Convolutional Neural Network on the remote sensing to extract features is also explained. Challenges that met CNN were discussed. Nº de ref. del artículo: 9786207447008
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Deep learning in Remote sensing | Convolutional Neural Network (CNNs)
Publicado por
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 620744700X
ISBN 13: 9786207447008
Nuevo
Taschenbuch
Librería: preigu, Osnabrück, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Deep learning in Remote sensing | Convolutional Neural Network (CNNs) | Lamyaa Taha (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786207447008 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 128164076
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