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.

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.

Autorentext

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.

Inhalt

Chapter 1 Introduction. 1

1.1 AI's Historical Changes 1

1.2 What Is Deep Learning?. 3

1.3 Practical Applications of Deep Learning. 4

1.4 Structure of the Book. 7 1.5 Introduction to MindSpore. 7

Chapter 2 Deep Learning Basics. 18

2.1 Regression Algorithms. 18

2.2 Gradient Descent 21 2.3 Classification Algorithms. 25

2.4 Overfitting and Underfitting. 28

Chapter 3 DNN.. 32

3.1 Feedforward Network. 32

3.2 Backpropagation. 34

3.3 Generalization Ability. 38

3.4 Implementing Simple Neural Networks Using MindSpore. 39

Chapter 4 Training of DNNs. 45

4.1 Main Challenges to Deep Learning Systems 45

4.2 Regularization. 48

4.3 Dropout 51

4.4 Adaptive Learning Rate. 55

4.5 Batch Normalization. 59

4.6 Implementing DNNs Using MindSpore. 61

Chapter 5 Convolutional Neural Network. 66

5.1 Convolution. 66

5.2 Pooling. 69

5.3 Residual Network. 71

5.4 Application: Image Classification. 74

5.5 Implementing Image Classification Based on the
DNN Using MindSpore. 79

Chapter 6 RNN.. 89

6.1 Overview.. 89

6.2 Deep RNN.. 90

6.3 Challenges of Long-Term Dependency. 91 6.4 &n...