Computational Methods for Deep Learning

Yan, Wei Qi

ISBN 10: 9819948223 / ISBN 13: 9789819948222

Verlag: Springer, 2023

Beschreibung

This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Bestandsnummer des Verkäufers ABNR-283317

Bibliografische Details

Titel: Computational Methods for Deep Learning

Verlag: Springer

Erscheinungsdatum: 2023

Einband: Hardcover

Zustand: New

Auflage: 2. Auflage

Über diesen Titel

Inhaltsangabe:

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. 

The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). 

This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

Über die Autorin bzw. den Autor:

Wei Qi Yan is Director of Institute of Robotics & Vision (IoRV) at Auckland University of Technology (AUT) in New Zealand (NZ). Dr. Yan's research interests encompass deep learning, intelligent surveillance, computer vision, and multimedia computing. His expertise lies in computational mathematics, applied mathematics, computer science, and computer engineering. He holds the positions of Chief Technology Officer (CTO) of Screen 2 Script Limited (NZ) and Director and Chief Scientist of the Joint Laboratory between AUT and Shandong Academy of Sciences China (NZ). Dr. Yan also serves as Chair of ACM Multimedia Chapter of New Zealand and is Member of the ACM. Additionally, he is Senior Member of the IEEE and TC Member of the IEEE. In 2022, Dr. Yan was recognized as one of the world’s top 2% cited scientists by Stanford University.