Deep Learning - Weidong Kuang

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Résumé :

Preface xv
Mathematical Notation xxi

1 Introduction to Deep Learning 1
1.1 Introduction 1
1.2 Types of Machine Learning 2
1.3 Data Representation in Machine Learning 6
1.4 An Overview of Deep Learning 8
1.5 Resources for Deep Learning 14

2 Linear Regression 19
2.1 Linear Regression with Single Feature 19
2.2 Linear Regression with Multiple Features 25
2.3 Linear Models for Regression 28
2.4 Linear Regression - a Probabilistic Perspective View 31
2.5 An Example: House Price Prediction 35
2.6 Summary and Further Reading 41

3 Classification and Logistic Regression 45
3.1 Logistic Regression 45
3.2 Performance Metrics for Classification 52
3.3 Implementation of Logistic Regression in Python 56
3.4 Summary 61

4 Basics of Neural Networks 67
4.1 A Simplest Neural Network: A Logistic Regression Unit 67
4.2 From Regression to Neural Networks 69
4.3 Neural Network Representation: Feedforward Propagation 72
4.4 Activation Functions 73
4.5 Network Training: Backward Propagation 76
4.6 Multi-class Classification: Softmax and Cross-Entropy Loss 79
4.7 Practice in Python 82
4.8 Summary and Further Reading 100

5 Practical Considerations in Neural Networks 107
5.1 Multiple-Layer Neural Networks 108
5.2 Generalization and Model Selection 111
5.3 Regularization 115
5.4 Weight Initialization 119
5.5 Mini-batch Gradient Descent 122
5.6 Normalization 124
5.7 Adam Optimization 129
5.8 Gradient Checking 132
5.9 Examples in Python 133
5.10 Summary and Further Reading 166

6 Introduction to PyTorch 171
6.1 Why PyTorch? 171
6.2 Tensors 172
6.3 Data Representation Using Tensors 184
6.4 Linear Regression Using PyTorch 189
6.5 Neural Networks Using PyTorch 198
6.6 Summary and Further Reading 203

7 Convolutional Neural Networks 205
7.1 Architecture of Convolutional Neural Networks 205
7.2 Convolution Layer 207
7.3 Pooling Layer and Fully Connected Layer 212
7.4 Backpropagation in CNNs (Optional) 214
7.5 Batch Normalization for CNNs 222
7.6 Implement CNNs in PyTorch 223
7.7 Summary and Further Reading 234

8 Classic Architectures of CNNs 239
8.1 Datasets 239
8.2 AlexNet 243
8.3 VGG: Networks Using Blocks 246
8.4 GoogLeNet 249
8.5 ResNet 250
8.6 Pretrained Models 253
8.7 Summary and Further Reading 265

9 Object Detection - YOLO 269
9.1 Introduction 269
9.2 YOLO (v1) 270
9.3 YOLO (v2) 276
9.4 YOLO (v3) 280
9.5 Implementation of YOLO v3 Using Pre-trained Model 289
9.6 A Metric for Object Detection: mAP 310
9.7 Summary and Further Reading 314

10 Introduction to Probabilistic Generative Models 319
10.1 Generative Models with Latent Variables 320
10.2 EM Algorithm 323
10.3 Variational Auto-encoder (VAE) 333
10.4 VAE on MNIST Dataset in PyTorch 340
10.5 Summary and Further Reading 348

11 Generative Adversarial Networks 351
11.1 Mathematical Description of the Original GAN 351
11.2 Implementation of GANs 354
11.3 Practical Issues with the Original GAN 362
11.4 Conditional GAN 362
11.5 InfoGAN 364
11.6 Wasserstein GAN 367
11.7 CycleGAN 372
11.8 f-GANs 375
11.9 Example: Deep Convolutional GAN on MNIST Dataset 378
11.10 Summary and Further Reading 394

12 Diffusion Models 399
12.1 Revisit Variational Auto-Encoder 399
12.3 Score-Based Generative Modeling 409
12.4 Denoising Diffusion Implicit Models for Acceleration 417
12.5 Guidance 421
12.6 Implementation of a Simple Diffusion Model on MNIS...

Biographie:

Weidong Will Kuang, PhD, is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Texas, Rio Grande Valley. He is an expert in signal processing, deep learning, and integrated circuits....