Computational Methods for Deep Learning - Theory, Algorithms, and Implementations (2nd ed. 2023)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Texts in Computer Science
Release date:	September 2023
First published:	2023
Authors:	Weiqi Yan
Dimensions:	235 x 155mm (L x W)
Pages:	210
Edition:	2nd ed. 2023
ISBN-13:	978-981-9948-22-2
Subtitles:	English
Categories:	Books
LSN:	981-9948-22-3
Barcode:	9789819948222

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