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.…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
