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Deep learning and practice with Mind...

Chen, Lei.

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Deep learning and practice with MindSpore

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning and practice with MindSpore/ by Lei Chen.
作者:	Chen, Lei.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xviii, 394 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-16-2233-5
ISBN:	9789811622335

Deep learning and practice with MindSpore
Chen, Lei.

Deep learning and practice with MindSpore[electronic resource] /by Lei Chen. - Singapore :Springer Singapore :2021. - xviii, 394 p. :ill. (some col.), digital ;24 cm. - Cognitive intelligence and robotics,2520-1964. - Cognitive intelligence and robotics..

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.

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.

ISBN: 9789811622335

Standard No.: 10.1007/978-981-16-2233-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .C54 2021

Dewey Class. No.: 006.31

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505 0 $a 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.
520 $a 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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