Deep Learning and Practice with Mindspore (2021)Hardcover - 2021, 18 August 2021

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 LSTM Network and GRU.. 93

6.5 Application: Text Prediction. 96

6.6 Implementing Text Prediction Based on LSTM Using MindSpore. 97

Chapter 7 Unsupervised Learning: Word Vector. 101

7.1 Word2Vec. 102

7.2 GloVe. 114

7.3 Transformer 121

7.4&