Computational methods for deep learning: theory, algorithms, and implementations / by Wei Qi Yan

By:

Yan, Wei Qi

Series: Texts in computer sciencePublication details: Singapore: Springer Nature, 2023.Edition: 2nd editionDescription: xx, 222 p. 24 cmISBN:

9789819948222

Subject(s):

Summary: 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.

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Item type	Current library	Collection	Call number	Status	Notes	Barcode
Reference	IIITDM Kancheepuram Library Reference	Reference	004.85 YAN (Browse shelf(Opens below))	Available	CSE	0008247

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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.

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