Algorithms of the Intelligent Web

by Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

Overview

Summary

Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction.

About the Book

Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.

What's Inside

• Introduction to machine learning
• Extracting structure from data
• Deep learning and neural networks
• How recommendation engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a machine learning expert and data science practitioner in the field of online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Foreword by Yike Guo.

Table of Contents

1. Building applications for the intelligent web
2. Extracting structure from data: clustering and transforming your data
3. Recommending relevant content
4. Classification: placing things where they belong
5. Case study: click prediction for online advertising
6. Deep learning and neural networks
7. Making the right choice
8. The future of the intelligent web
9. Appendix - Capturing data on the web

Product Details

ISBN-13:	9781617292583
Publisher:	Manning Publications Company
Publication date:	09/08/2016
Pages:	240
Product dimensions:	7.30(w) x 9.10(h) x 0.60(d)

About the Author

Douglas McIlwraith earned his first degree at Cambridge in computer science before completing a PhD in sensor fusion from Imperial College in London. He is a machine learning expert, currently working as senior data scientist for a London-based advertising company.

Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. He has 25 years experience in developing professional software.

Dmitry Babenko has designed and built a wide variety of applications and infrastructure frameworks for banking, insurance, supply-chain management, and business intelligence companies. He received a M.S. degree in computer science from Belarussian State University of Informatics and Radioelectronics.

Table of Contents

Foreword ix

Preface xi

Acknowledgments xiii

About this book xv

1 Building applications for the intelligent web 1

1.1 An intelligent algorithm in action: Google Now 3

1.2 The intelligent algorithm lifecycle 5

1.3 Further examples of intelligent algorithms 6

1.4 Things that intelligent applications are not 7

Intelligent algorithms are not all-purpose thinking machines 7

Intelligent algorithms are not a drop-in replacement for humans 7

Intelligent algorithms are not discovered by accident 8

1.5 Classes of intelligent algorithm 8

Artificial intelligence 9

Machine learning 9

Predictive analytics 10

1.6 Evaluating the performance of intelligent algorithms 12

Evaluating intelligence 12

Evaluating predictions 12

1.7 Important notes about intelligent, algorithms 15

Your data is not reliable 15

Inference does not happen instantaneously 16

Size matters! 16

Different algorithms have different scaling characteristics 16

Everything is not a nail! 17

Data isn't everything 17

Training time can be variable 17

Generalization is the goal 17

Human intuition is problematic 18

Think about engineering new features 18

Learn many different models 18

Correlation is not the same, as causation 18

1.8 Summary 19

2 Extracting structure from data: clustering and transforming your data 20

2.1 Data, structure, bias, and noise 22

2.2 The curse of dimensionality 25

2.3 K-means 26

K-means in action 31

2.4 The Gaussian mixture model 33

What is the Gaussian distribution? 34

Expectation maximization and the Gaussian distribution 36

The Gaussian mixture model 36

An example of learning using a Gaussian mixture model 38

2.5 The relationship between k-means and GMM 41

2.6 Transforming the data axis 42

Eigenvectors and eigenvalues 42

Principal component analysis 43

An example of principal component analysis 44

2.7 Summary 46

3 Recommending relevant content 47

3.1 Setting the scene: an online movie store 48

3.2 Distance and similarity 49

A closer look at distance and similarity 53

Which is the best similarity formula? 55

3.3 How do recommender engines work? 56

3.4 User-based collaborative Filtering 57

3.5 Model-based recommendation using singular value decomposition 62

Singular valve decomposition 63

Recommendation using SVD: choosing movies for a given user 64

Recommendation using SVD: choosing users for a given movie 69

3.6 The Netflix Prize 72

3.7 Evaluating your recommendation 74

3.8 Summary 75

4 Classification: placing things where they belong 77

4.1 The need for classification 78

4.2 An overview of classifiers 81

Structural classification algorithms 82

Statistical classification algorithms 84

The lifecyele of a classifier 85

4.3 Fraud detection with logistic regression 86

A linear regression primer 86

From linear to logistic regression 88

Implementing fraud detection 91

4.4 Are your results credible? 99

4.5 Classification with very large datasets 103

4.6 Summary 105

5 Case study: click prediction for online advertising 106

5.1 History and background 107

5.2 The exchange 109

Cookie matching 110

Bid 110

Bid win (or loss) notification 111

Ad placement 111

Ad monitoring 111

5.3 What is a bidder? 112

Requirements of a bidder 112

5.4 What is a decisioning engine? 113

Information about the user 113

Information about the placement 114

Contextual information 114

Data preparation 114

Decisioning engine model 114

Mapping predicted click-through rate to bid price 115

Feature engineering 115

Model training 116

5.5 Click prediction with Vowpal Wabbit 116

Vowpal Wabbit data formal 117

Preparing the dataset 119

Testing the model 124

Model calibration 126

5.6 Complexities of building a decisioning engine 128

5.7 The future of real-time prediction 129

5.8 Summary 130

6 Deep learning and neural networks 131

6.1 An intuitive approach to deep learning 132

6.2 Neural networks 133

6.3 The perceptron 135

Training 136

Training a perceptron in scikit-learn 138

A geometric interpretation of the perceptron for two inputs 140

6.4 Multilayer perceptrons 142

Training using backpropagation 145

Activation functions 146

Intuition behind backpropagation 147

Backpropagation theory 148

MLNN in scikit-learn 150

A learned MLP 153

6.5 Going deeper: from multilayer neural networks to deep learning 154

Restricted Boltzmann Machines 154

The Bernoulli Restricted Boltzmann Machine 155

RBMs in action 158

6.6 Summary 161

7 Making the right choice 162

7.1 A/B testing 163

The theory 164

The code 166

Suitability of A/B 168

7.2 Multi-armed bandits 169

Multi-armed bandit strategies 169

7.3 Bayesian bandits in the wild 174

7.4 A/B vs the Bayesian bandit 185

7.5 Extensions to multi-armed bandits 186

Contextual bandits 186

Adversarial bandits 187

7.6 Summary 188

8 The future of the intelligent web 189

8.1 Future applications of the intelligent web 190

The internet of things 190

Home healthcare 191

The self-driving vehicle 191

Personalized physical advertising 191

The semantic, web 192

8.2 Social implications of the intelligent web 193

Appendix Capturing data on the web 194

Index 217