TY  - GEN
N2  - 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.
DO  - 10.1007/978-981-99-4823-9
DO  - doi
AB  - 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.
T1  - Computational methods for deep learning :theory, algorithms, and implementations /
AU  - Yan, Wei Qi,
ET  - Second edition.
CN  - Q325.5
ID  - 1480972
KW  - Apprentissage automatique.
KW  - Réseaux neuronaux (Informatique)
KW  - Exploration de données (Informatique)
KW  - Données volumineuses.
KW  - Informatique
KW  - Machine learning.
KW  - Neural networks (Computer science)
KW  - Data mining.
KW  - Big data.
KW  - Computer science
SN  - 9789819948239
SN  - 9819948231
TI  - Computational methods for deep learning :theory, algorithms, and implementations /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4823-9
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4823-9
ER  -