Deep Learning-Powered Technologies: Autonomous Driving, Artificial Intelligence of Things (AIoT), Augmented Reality, 5G Communications and Beyond - Brossura

Mohamed, Khaled Salah

 
9783031357398: Deep Learning-Powered Technologies: Autonomous Driving, Artificial Intelligence of Things (AIoT), Augmented Reality, 5G Communications and Beyond
Brossura
ISBN 10: 3031357396 ISBN 13: 9783031357398
Casa editrice: Springer, 2024

Sinossi

This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Khaled Salah Mohamed attended the school of engineering, Department of Electronics and Communications at Ain-Shams University from 1998 to 2003, where he received his B.Sc. degree in Electronics and Communications Engineering with distinction and honors. He received his Master's degree in Electronics from Cairo University, Egypt in 2008. He received his PhD degree in 2012. Dr. Khaled Salah is currently an Applications Engineering Consultant for Siemens Digital Industries Software, in Freemont, CA. Dr. Khaled Salah has published a large number of papers in in the top refereed journals and conferences. His research interests are in 3D integration, IP Modeling, and SoC design.

Dalla quarta di copertina

This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.


In addition, this book:
  • Discusses fundamental techniques and tools for deep learning, including a deep dive into the Python language
  • Explores the state-of-the-art applications of machine and deep learning
  • Includes a comparative study of different deep learning architectures, applications, tools, and platforms

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2024
Lingua
Inglese
ISBN 10 
3031357396
ISBN 13 
9783031357398
Rilegatura
Copertina flessibile
Numero di pagine
224
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