Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

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.


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Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

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.


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Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

by Wei Qi Yan
Computational Methods for Deep Learning: Theory, Algorithms, and Implementations
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Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

Computational Methods for Deep Learning: Theory, Algorithms, and Implementations

by Wei Qi Yan

Overview

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.


Product Details

ISBN-13: 9789819948239
Publisher: Springer-Verlag New York, LLC
Publication date: 09/15/2023
Series: Texts in Computer Science
Sold by: Barnes & Noble
Format: eBook
File size: 26 MB
Note: This product may take a few minutes to download.

About the Author

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.


Table of Contents

1. Introduction.-  2. Deep Learning Platforms.- 3.  CNN and RNN.- 4. Autoencoder and GAN.- 5. Reinforcement Learning.- 6. CapsNet and Manifold Learning.- 7. Boltzmann Machines.- 8. Transfer Learning and Ensemble Learning.

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