Phil Kim, PhD is an experienced MATLAB programmer and user.  He also works with algorithms of large data sets drawn from AI, machine learning.  He has worked at Korea Aerospace Research Institute as a Senior Researcher. There, his main task was to develop autonomous flight algorithm and onboard software for unmanned aerial vehicle. An on-screen keyboard program named 'Clickey' was developed by him during his period in PhD program and served as a bridge to bring the author currently to his current assignment as a Senior Research Officer at National Rehabilitation Research Institute of Korea.

Contents at a Glance 4
Contents 5
About the Author 8
About the Technical Reviewer 9
Acknowledgments 10
Introduction 11
Chapter 1: Machine Learning 14
What Is Machine Learning? 15
Challenges with Machine Learning 17
Overfitting 19
Confronting Overfitting 23
Types of Machine Learning 25
Classification and Regression 27
Summary 30
Chapter 2: Neural Network 32
Nodes of a Neural Network 33
Layers of Neural Network 35
Supervised Learning of a Neural Network 40
Training of a Single-Layer Neural Network: Delta Rule 42
Generalized Delta Rule 45
SGD, Batch, and Mini Batch 47
Stochastic Gradient Descent 47
Batch 48
Mini Batch 49
Example: Delta Rule 50
Implementation of the SGD Method 51
Implementation of the Batch Method 54
Comparison of the SGD and the Batch 56
Limitations of Single-Layer Neural Networks 58
Summary 63
Chapter 3: Training of Multi-Layer Neural Network 65
Back-Propagation Algorithm 66
Example: Back-Propagation 72
XOR Problem 74
Momentum 77
Cost Function and Learning Rule 80
Example: Cross Entropy Function 85
Cross Entropy Function 86
Comparison of Cost Functions 88
Summary 91
Chapter 4: Neural Network and Classification 93
Binary Classification 93
Multiclass Classification 98
Example: Multiclass Classification 105
Summary 114
Chapter 5: Deep Learning 115
Improvement of the Deep Neural Network 117
Vanishing Gradient 117
Overfitting 119
Computational Load 121
Example: ReLU and Dropout 121
ReLU Function 122
Dropout 126
Summary 132
Chapter 6: Convolutional Neural Network 133
Architecture of ConvNet 133
Convolution Layer 136
Pooling Layer 142
Example: MNIST 143
Summary 159
Index 160