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Deep Learning and Practice with MindSpore

Chen, Lei
Innbundet / 2021 / Engelsk

Produktdetaljer

ISBN
9789811622328
Publisert
2021-08-18
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Orginaltittel
深度学习与MindSpore实践

Forfatter
Chen, Lei
Oversetter
Zeng, Yunhui

Om bidragsyterne

Chen Lei is a Chair Professor of the Department of Computer Science and Engineering and the Director of the Big Data Institute at Hong Kong University of Science and Technology (HKUST). His research focuses on data-driven AI, human-powered machine learning, knowledge graphs, and data mining on social media. He has published more than 400 papers in world-renowned journals and conference proceedings and won the 2015 SIGMOD Test of Time Award. Currently, he serves as the Editor-in-Chief of the VLDB 2019 Journal, the Associate Editor-in-Chief of the IEEE TKDE Journal, and an executive member of the VLDB Endowment. He is also IEEE Fellow and ACM Distinguished Scientist.

Deep Learning and Practice with MindSpore

Chen, Lei
Innbundet / 2021 / Engelsk
Chen, Lei
Innbundet / 2021 / Engelsk
Nettpris:
2.538,-
Levering 7-20 dager
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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.
Les mer
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.
Les mer
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.
Les mer
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.
Les mer
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
Les mer

Relaterte produkter

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.
Les mer
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.
Les mer
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.
Les mer
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.
Les mer
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
Les mer

Produktdetaljer

ISBN
9789811622328
Publisert
2021-08-18
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Orginaltittel
深度学习与MindSpore实践

Forfatter
Chen, Lei
Oversetter
Zeng, Yunhui

Om bidragsyterne

Chen Lei is a Chair Professor of the Department of Computer Science and Engineering and the Director of the Big Data Institute at Hong Kong University of Science and Technology (HKUST). His research focuses on data-driven AI, human-powered machine learning, knowledge graphs, and data mining on social media. He has published more than 400 papers in world-renowned journals and conference proceedings and won the 2015 SIGMOD Test of Time Award. Currently, he serves as the Editor-in-Chief of the VLDB 2019 Journal, the Associate Editor-in-Chief of the IEEE TKDE Journal, and an executive member of the VLDB Endowment. He is also IEEE Fellow and ACM Distinguished Scientist.

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