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- Author: Wei Qi Yan
- Publisher: Springer
- ISBN-10: 9819948223
- ISBN-13: 9789819948222
- Format: 15.6 x 23.4 x 1.4 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 21d.02:43:34
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Wei Qi Yan
- Publisher: Springer
- ISBN-10: 9819948223
- ISBN-13: 9789819948222
- Format: 15.6 x 23.4 x 1.4 cm, kieti viršeliai
- Language: English English
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.
Reviews