Deep Learning and Practice with MindSpore / by Lei Chen
By: Chen, Lei, autor
Material type:
E-bookSeries: (Cognitive Intelligence and Robotics, 2520-1964); (Computer Science (SpringerNature-11645)); (Computer Science (R0) (SpringerNature-43710)).Publisher: Singapore : Springer International Publising, 2021Edition: First edition 2021.Description: 1 recurso en línea (XVIII, 394 páginas) : 357 ilustraciones, 13 ilustraciones a color.ISBN: 9789811622335.Subject: Aprendizaje automático
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | Q325.5 2021 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.19122075 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| Q325.5 2021 EB Detecting Trust and Deception in Group Interaction | Q325.5 2021 EB Deep Learning for Human Activity Recognition : Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings | Q325.5 2021 EB Automated Design of Machine Learning and Search Algorithms | Q325.5 2021 EB Deep Learning and Practice with MindSpore | Q325.5 2021 EB Machine Learning | Q325.5 2021 EB Explainable AI with Python | Q325.5 2021 EB Machine Learning Modeling for IoUT Networks : Internet of Underwater Things |
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.
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