Machine Learning and AI with Simple Python and MATLAB Scripts - Uyar, M Umit

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Résumé :

About the Author xiii

Preface xv

Acknowledgments xvii

About the Companion Website xix

1 Introduction 1

1.1 Artificial Intelligence 1

1.2 A Historical Perspective 1

1.3 Principles of AI 2

1.4 Applications That Are Impossible Without AI 2

1.5 Organization of This Book 3

2 Artificial Neural Networks 7

2.1 Introduction 7

2.2 Applications of ANNs 7

2.3 Components of ANNs 8

2.3.1 Neurons 8

2.3.2 Sigmoid Activation Function 9

2.3.3 Rectilinear Activation Function 9

2.3.4 Weights of Synapses 10

2.4 Training an ANN 11

2.5 Forward Propagation 12

2.5.1 Forward Propagation from Input to Hidden Layer 13

2.6 Back Propagation 13

2.6.1 Back Propagation for a Neuron 13

2.6.2 Back Propagation - from Output to Hidden Layer 15

2.6.3 Back Propagation - from Hidden Layer to Input 16

2.7 Updating Weights 17

2.8 ANN with Input Bias 17

2.9 A Simple Algorithm for ANN Training 18

2.10 Computational Complexity of ANN Training 18

2.11 Normalization of ANN Inputs and Outputs 19

2.12 Concluding Remarks 20

2.13 Exercises for Chapter 2 20

3 ANNs for Optimized Prediction 23

3.1 Introduction 23

3.2 Selection of ANN Inputs 24

3.3 Selection of ANN Outputs 24

3.4 Construction of Hidden Layers 25

3.5 Case Study 1: Sleep-Study Example 25

3.5.1 Using Matrices for ANN Training 26

3.5.2 Forward Propagation 28

3.5.3 Back Propagation 28

3.5.4 Updating Weights 29

3.5.5 Forward Propagation with New Weights 29

3.5.6 Back Propagation with New Weights 30

3.5.7 Using Normalized Input and Output Values 31

3.5.8 Reducing Errors During Training 34

3.5.9 Implementation of Sleep-Study ANN in Python 34

3.5.10 Implementation of Sleep-Study ANN in Matlab 37

3.6 Case Study 2: Prediction of Bike Rentals 41

3.6.1 Python Script for Bike Rentals Using an ANN 41

3.6.2 Matlab Script for Bike Rentals Using an ANN 46

3.7 Concluding Remarks 48

3.8 Exercises for Chapter 3 48

4 ANNs for Financial Stock Trading 51

4.1 Introduction 51

4.2 Programs that Buy and Sell Stocks 51

4.3 Technical Indicators 51

4.3.1 Simple Moving Average 52

4.3.2 Momentum 53

4.3.3 Exponential Moving Average 54

4.3.4 Bollinger Bands 54

4.4 A Simple Algorithmic Trading Policy 55

4.5 A Simple ANN for Algorithmic Stock Trading 57

4.5.1 ANN Inputs and Outputs 57

4.5.2 ANN Architecture 58

4.6 Python Script for Stock Trading Using an ANN 59

4.7 Matlab Script for Stock Trading Using an ANN 63

4.8 Concluding Remarks 65

4.9 Exercises for Chapter 4 65

5 ANNs for Alzheimer's Disease Prognosis 67

5.1 Introduction 67

5.2 Alzheimer's Disease 67

5.3 A Simple ANN for AD Prognosis 68

5.4 Python Script for AD Prognosis Using an ANN 71

5.5 Matlab Script for AD Prognosis Using an ANN 75

5.6 Concluding Remarks 80

5.7 Exercises for Chapter 5 81

6 ANNs for Natural Language Processing 83

6.1 Introduction 83

6.2 Impact of Text Messages on Stock Markets 84

6.3 A Simple ANN for NLP 85

6.3.1 ANN Inputs and Outputs 85

6.3.2 Keywords 85

6.3.3 Formation of Training Data 86

6.3.4 ANN Architecture 88

6.4 Python Script for NLP Using an ANN 89

6.5 Matlab Script for NLP Using an ANN 92

6.6 Concluding Remarks 96

6.7 Exercises for Chapter 6 97

7 Convolutional Neural Networks 99

7.1 Introduction 9...

Sommaire:

M. ?mit Uyar is a Professor at the City College of the City University of New York, USA. Dr. Uyar is an IEEE Fellow, author, co-author and co-editor of seven books, holder of seven U.S. patents, and developer of AI and game theory-based algorithms for applications in topology control in mobile networks and personalized cancer treatment....