Deep learning for engineers / Tariq M. Arif, Md Adilur Rahim

By:

Arif, Tariq M [VerfasserIn]

Contributor(s):

Rahim, Md. Adilur [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Boca Raton ; Abingdon : CRC Press, 2024Edition: First editionDescription: 1 Online-RessourceISBN:

9781003849803
1003849806
9781003402923
1003402925
9781003849827
1003849822

Subject(s):

Additional physical formats: 9781032515816. | 9781032504735. | Erscheint auch als: Deep learning for engineers. Druck-Ausgabe First edition. Boca Raton : CRC Press, 2024. xii, 157 SeitenDDC classification:

620.0072 23/eng/20240111
006.3/1 23/eng/20240301

LOC classification:

TA160
TA347.A78

Online resources:

Zugang im Netz des KIT

Summary: 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 usefulPPN: PPN: 1907960139Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB

Holdings ( 0 )

No physical items for this record