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Deep Learning and Practice with MindSpore de Lei Chen

Deep Learning and Practice with MindSpore: Cognitive Intelligence and Robotics

Autor Lei Chen Traducere de Yunhui Zeng
en Limba Engleză Hardback – 18 aug 2021
This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.
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Specificații

ISBN-13: 9789811622328
ISBN-10: 9811622329
Pagini: 412
Ilustrații: XVIII, 394 p. 357 illus., 13 illus. in color.
Dimensiuni: 160 x 241 x 28 mm
Greutate: 0.73 kg
Ediția:1st ed. 2021
Editura: Springer
Colecția Cognitive Intelligence and Robotics
Seria Cognitive Intelligence and Robotics

Locul publicării:Singapore, Singapore

Cuprins

Chapter 1. Introduction.- Chapter 2. Deep Learning Basics.- Chapter 3. DNN.- Chapter 4. Training of DNNs.- Chapter 5. Convolutional Neural Network.- Chapter 6. RNN.- Chapter 7. Unsupervised Learning: Word Vector.- Chapter 8. Unsupervised Learning: Graph Vector.- Chapter 9. Unsupervised Learning: Deep Generative Model.- Chapter 10. Deep Reinforcement Learning.- Chapter 11. Automated Machine Learning.- Chapter 12. Device-Cloud Collaboration.- Chapter 13. Deep Learning Visualization.- Chapter 14. Data Preparation for Deep Learning.

Notă biografică

Yingxia Shao is a Research Associate Professor at the School of Computer Science, Beijing University of Posts and Telecommunications. His research interests include large-scale graph analysis, knowledge graph management and representation, and parallel computing. He obtained his PhD from Peking University in 2016, under the supervision of Prof. Bin Cui. He worked with Prof. Lei Chen as a visiting scholar at HKUST in 2013 and 2014. He has served in the Technical Program Committee of various international conferences including VLDB, KDD, AAAI, IJCAI, DASFAA, BigData, APWeb-WAIM and MDM. He is serving as a reviewer of international journals including VLDBJ, DAPD, WWWJ, DSE. He was selected for a Google PhD Fellowship (2014), MSRA Fellowship (2014), PhD National Scholarship of MOE China (2014), ACM SIGMOD China Doctoral Dissertation Award (2017). He is currently a member of the ACM, IEEE, CCF, and China Database technical committee. Bin Cui is a Professor at the School of EECS and Director of the Institute of Network Computing and Information Systems, at Peking University. He obtained his B.Sc. from Xi'an Jiaotong University (Pilot Class) in 1996, and Ph.D. from National University of Singapore in 2004 respectively. From 2004 to 2006, he worked as a Research Fellow in Singapore-MIT Alliance. His research interests include database system architectures, query and index techniques, and big data management and mining. He has served in the Technical Program Committee of various international conferences including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015 and WAIM 2016. He is currently serving as a Trustee Board Member of VLDB Endowment, , is on the the Editorial Board of VLDB Journal, Distributed and Parallel Databases Journal, and Information Systems, and was formerly an associate editorof IEEE Transactions on Knowledge and Data Engineering (TKDE, 2009-2013). He was selected for a Microsoft Young Professorship award (MSRA 2008), CCF Young Scientist award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed a Cheung Kong distinguished Professor by the MOE in 2016. He is a senior member of the IEEE, member of the ACM and distinguished member of the CCF. Lei Chen received the BS degree in computer science and engineering from Tianjin University, Tianjin, China, in 1994, the MA degree from Asian Institute of Technology, Bangkok, Thailand, in 1997, and the Ph.D. degree in computer science from the University of Waterloo, Canada, in 2005. He is currently a Full Professor at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include crowdsourcing, social media analysis, probabilistic and uncertain databases, and privacy-preserved data publishing.The system developed by his team won the excellent demonstration award at the VLDB 2014. He was selected for the SIGMOD Test-of-Time Award in 2015. He is PC Track chairs for SIGMOD 2014, VLDB 2014, ICDE 2012, CIKM 2012, SIGMM 2011. He has served as PC members for SIGMOD, VLDB, ICDE, SIGMM, and WWW. Currently, he serves as PC co-chair for VLDB 2019, Editor-in-Chief of VLDB Journal and associate editor-in-chief of IEEE Transactions on Data and Knowledge Engineering. He is an IEEE fellow, a member of the VLDB endowment and an ACM Distinguished Scientist.

Textul de pe ultima copertă

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

Caracteristici

Introduces readers to deep learning models and algorithms in both theory and practice Explores how deep learning methods can be used in various applications and their performance in this regard Combines theory and practical applications to explain how to implement high-performance deep learning models and achieves effective learning with MindSpore, Huawei's self-developed deep learning computing framework