| Titre : |
Advanced applied deep learning : convolutional neural networks and object detection |
| Type de document : |
texte imprimé |
| Auteurs : |
Umberto Michelucci |
| Editeur : |
New York ; Berkeley, CA : Apress |
| Année de publication : |
2019 |
| Autre Editeur : |
Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer |
| Importance : |
xviii-285 p. |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-4842-4975-8 |
| Langues : |
Anglais (eng) |
| Descripteurs : |
Algorithme mathématique , Apprentissage automatique , Intelligence artificielle
|
| Tags : |
Deep Learning |
| Index. décimale : |
88 Intelligence artificielle |
| Résumé : |
Develop and optimize deep learning models with advanced architectures. This book teaches the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. It explains study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, it shows the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, it also offers tools how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in different models. Finally, it teaches how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book will have implemented various models in Keras and learned many advanced tricks for the next level. [D'après le résumé de l'éditeur] |
| En ligne : |
https://www.springer.com/fr/book/9781484249758 |
| Permalink : |
https://genes.bibli.fr/index.php?lvl=notice_display&id=150114 |
Advanced applied deep learning : convolutional neural networks and object detection [texte imprimé] / Umberto Michelucci . - New York ; Berkeley, CA : Apress : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2019 . - xviii-285 p. : ill. ; 24 cm. ISBN : 978-1-4842-4975-8 Langues : Anglais ( eng)
| Descripteurs : |
Algorithme mathématique , Apprentissage automatique , Intelligence artificielle
|
| Tags : |
Deep Learning |
| Index. décimale : |
88 Intelligence artificielle |
| Résumé : |
Develop and optimize deep learning models with advanced architectures. This book teaches the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. It explains study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, it shows the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, it also offers tools how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in different models. Finally, it teaches how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book will have implemented various models in Keras and learned many advanced tricks for the next level. [D'après le résumé de l'éditeur] |
| En ligne : |
https://www.springer.com/fr/book/9781484249758 |
| Permalink : |
https://genes.bibli.fr/index.php?lvl=notice_display&id=150114 |
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