Databases / Data management Books

Book SynopsisWith this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package.The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distrTable of ContentsIntroduction. The Mother of Invention’s Triplets: Moore’s Law, the Proliferation of Data, and Data Storage Technology. Hadoop. HBase and Other Big Data Databases. Machine Learning. Statistics. Google. Geographic Information Systems. Discovery. Data Quality. Benefits. Concerns.

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Book SynopsisData-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visuali

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Book SynopsisUpdated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.

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Book SynopsisThis book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.Trade Review'Chirag's extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a 'hands on' introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.' Theresa Dirndorfer Anderson'Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.' Ryen W. White, Microsoft Research AITable of ContentsPart I. Introduction: 1. Introduction; 2. Data; 3. Techniques; Part II. Tools: 4. UNIX; 5. Python; 6. R; 7. MySQL; Part III. Machine Learning: 8. Machine learning introduction and regression; 9. Supervised learning; 10. Unsupervised learning; Part IV. Applications and Evaluations: 11. Hands-on with solving data problems; 12. Data collection, experimentation and evaluation.

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Book SynopsisData Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use.Table of ContentsIntroduction xvii Chapter 1 Introduction to Big Data Analytics 1 1.1 Big Data Overview 2 1.1.1 Data Structures 5 1.1.2 Analyst Perspective on Data Repositories 9 1.2 State of the Practice in Analytics 11 1.2.1 BI Versus Data Science 12 1.2.2 Current Analytical Architecture 13 1.2.3 Drivers of Big Data 15 1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics 16 1.3 Key Roles for the New Big Data Ecosystem 19 1.4 Examples of Big Data Analytics 22 Summary 23 Exercises 23 Bibliography 24 Chapter 2 Data Analytics Lifecycle 25 2.1 Data Analytics Lifecycle Overview 26 2.1.1 Key Roles for a Successful Analytics Project 26 2.1.2 Background and Overview of Data Analytics Lifecycle 28 2.2 Phase 1: Discovery 30 2.2.1 Learning the Business Domain 30 2.2.2 Resources 31 2.2.3 Framing the Problem 32 2.2.4 Identifying Key Stakeholders 33 2.2.5 Interviewing the Analytics Sponsor 33 2.2.6 Developing Initial Hypotheses 35 2.2.7 Identifying Potential Data Sources 35 2.3 Phase 2: Data Preparation 36 2.3.1 Preparing the Analytic Sandbox 37 2.3.2 Performing ETLT 38 2.3.3 Learning About the Data 39 2.3.4 Data Conditioning 40 2.3.5 Survey and Visualize 41 2.3.6 Common Tools for the Data Preparation Phase 42 2.4 Phase 3: Model Planning 42 2.4.1 Data Exploration and Variable Selection 44 2.4.2 Model Selection 45 2.4.3 Common Tools for the Model Planning Phase 45 2.5 Phase 4: Model Building 46 2.5.1 Common Tools for the Model Building Phase 48 2.6 Phase 5: Communicate Results 49 2.7 Phase 6: Operationalize 50 2.8 Case Study: Global Innovation Network and Analysis (GINA) 53 2.8.1 Phase 1: Discovery 54 2.8.2 Phase 2: Data Preparation 55 2.8.3 Phase 3: Model Planning 56 2.8.4 Phase 4: Model Building 56 2.8.5 Phase 5: Communicate Results 58 2.8.6 Phase 6: Operationalize 59 Summary 60 Exercises 61 Bibliography 61 Chapter 3 Review of Basic Data Analytic Methods Using R 63 3.1 Introduction to R 64 3.1.1 R Graphical User Interfaces 67 3.1.2 Data Import and Export 69 3.1.3 Attribute and Data Types 71 3.1.4 Descriptive Statistics 79 3.2 Exploratory Data Analysis 80 3.2.1 Visualization Before Analysis 82 3.2.2 Dirty Data 85 3.2.3 Visualizing a Single Variable 88 3.2.4 Examining Multiple Variables 91 3.2.5 Data Exploration Versus Presentation 99 3.3 Statistical Methods for Evaluation 101 3.3.1 Hypothesis Testing 102 3.3.2 Difference of Means 104 3.3.3 Wilcoxon Rank-Sum Test 108 3.3.4 Type I and Type II Errors 109 3.3.5 Power and Sample Size 110 3.3.6 ANOVA 110 Summary 114 Exercises 114 Bibliography 115 Chapter 4 Advanced Analytical Theory and Methods: Clustering 117 4.1 Overview of Clustering 118 4.2 K-means 118 4.2.1 Use Cases 119 4.2.2 Overview of the Method 120 4.2.3 Determining the Number of Clusters 123 4.2.4 Diagnostics 128 4.2.5 Reasons to Choose and Cautions 130 4.3 Additional Algorithms 134 Summary 135 Exercises 135 Bibliography 136 Chapter 5 Advanced Analytical Theory and Methods: Association Rules 137 5.1 Overview 138 5.2 Apriori Algorithm 140 5.3 Evaluation of Candidate Rules 141 5.4 Applications of Association Rules 143 5.5 An Example: Transactions in a Grocery Store 143 5.5.1 The Groceries Dataset 144 5.5.2 Frequent Itemset Generation 146 5.5.3 Rule Generation and Visualization 152 5.6 Validation and Testing 157 5.7 Diagnostics 158 Summary 158 Exercises 159 Bibliography 160 Chapter 6 Advanced Analytical Theory and Methods: Regression 161 6.1 Linear Regression 162 6.1.1 Use Cases 162 6.1.2 Model Description 163 6.1.3 Diagnostics 173 6.2 Logistic Regression 178 6.2.1 Use Cases 179 6.2.2 Model Description 179 6.2.3 Diagnostics 181 6.3 Reasons to Choose and Cautions 188 6.4 Additional Regression Models 189 Summary 190 Exercises 190 Chapter 7 Advanced Analytical Theory and Methods: Classification 191 7.1 Decision Trees 192 7.1.1 Overview of a Decision Tree 193 7.1.2 The General Algorithm 197 7.1.3 Decision Tree Algorithms 203 7.1.4 Evaluating a Decision Tree 204 7.1.5 Decision Trees in R 206 7.2 Naïve Bayes 211 7.2.1 Bayes’ Theorem 212 7.2.2 Naïve Bayes Classifier 214 7.2.3 Smoothing 217 7.2.4 Diagnostics 217 7.2.5 Naïve Bayes in R 218 7.3 Diagnostics of Classifiers 224 7.4 Additional Classification Methods 228 Summary 229 Exercises 230 Bibliography 231 Chapter 8 Advanced Analytical Theory and Methods: Time Series Analysis 233 8.1 Overview of Time Series Analysis 234 8.1.1 Box-Jenkins Methodology 235 8.2 ARIMA Model 236 8.2.1 Autocorrelation Function (ACF) 236 8.2.2 Autoregressive Models 238 8.2.3 Moving Average Models 239 8.2.4 ARMA and ARIMA Models 241 8.2.5 Building and Evaluating an ARIMA Model 244 8.2.6 Reasons to Choose and Cautions 252 8.3 Additional Methods 253 Summary 254 Exercises 254 Chapter 9 Advanced Analytical Theory and Methods: Text Analysis 255 9.1 Text Analysis Steps 257 9.2 A Text Analysis Example 259 9.3 Collecting Raw Text 260 9.4 Representing Text 264 9.5 Term Frequency—Inverse Document Frequency (TFIDF) 269 9.6 Categorizing Documents by Topics 274 9.7 Determining Sentiments 277 9.8 Gaining Insights 283 Summary 290 Exercises 290 Bibliography 291 Chapter 10 Advanced Analytics—Technology and Tools: MapReduce and Hadoop 295 10.1 Analytics for Unstructured Data 296 10.1.1 Use Cases 296 10.1.2 MapReduce 298 10.1.3 Apache Hadoop 300 10.2 The Hadoop Ecosystem 306 10.2.1 Pig 306 10.2.2 Hive 308 10.2.3 HBase 311 10.2.4 Mahout 319 10.3 NoSQL 322 Summary 323 Exercises 324 Bibliography 324 Chapter 11 Advanced Analytics—Technology and Tools: In-Database Analytics 327 11.1 SQL Essentials 328 11.1.1 Joins 330 11.1.2 Set Operations 332 11.1.3 Grouping Extensions 334 11.2 In-Database Text Analysis 338 11.3 Advanced SQL 343 11.3.1 Window Functions 343 11.3.2 User-Defined Functions and Aggregates 347 11.3.3 Ordered Aggregates 351 11.3.4 MADlib 352 Summary 356 Exercises 356 Bibliography 357 Chapter 12 The Endgame, or Putting It All Together 359 12.1 Communicating and Operationalizing an Analytics Project 360 12.2 Creating the Final Deliverables 362 12.2.1 Developing Core Material for Multiple Audiences 364 12.2.2 Project Goals 365 12.2.3 Main Findings 367 12.2.4 Approach 369 12.2.5 Model Description 371 12.2.6 Key Points Supported with Data 372 12.2.7 Model Details 372 12.2.8 Recommendations 374 12.2.9 Additional Tips on Final Presentation 375 12.2.10 Providing Technical Specifications and Code 376 12.3 Data Visualization Basics 377 12.3.1 Key Points Supported with Data 378 12.3.2 Evolution of a Graph 380 12.3.3 Common Representation Methods 386 12.3.4 How to Clean Up a Graphic 387 12.3.5 Additional Considerations 392 Summary 393 Exercises 394 References and Further Reading 394 Bibliography 394 Index 397

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Book SynopsisMonetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projectsSecret, reverse-engineered data monetization tactics that no one's talking aboutThe shocking truth about how simple natural language processing can beHow to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.Table of ContentsIntroduction 1 Part 1: Getting Started with Data Science 5 Chapter 1: Wrapping Your Head Around Data Science 7 Chapter 2: Tapping into Critical Aspects of Data Engineering 19 Part 2: Using Data Science to Extract Meaning from Your Data 37 Chapter 3: Machine Learning Means Using a Machine to Learn from Data 39 Chapter 4: Math, Probability, and Statistical Modeling 51 Chapter 5: Grouping Your Way into Accurate Predictions 77 Chapter 6: Coding Up Data Insights and Decision Engines 103 Chapter 7: Generating Insights with Software Applications 137 Chapter 8: Telling Powerful Stories with Data 161 Part 3: Taking Stock of Your Data Science Capabilities 187 Chapter 9: Developing Your Business Acumen 189 Chapter 10: Improving Operations 205 Chapter 11: Making Marketing Improvements 229 Chapter 12: Enabling Improved Decision-Making 245 Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes 265 Chapter 14: Monetizing Data and Data Science Expertise 275 Part 4: Assessing Your Data Science Options 289 Chapter 15: Gathering Important Information about Your Company 291 Chapter 16: Narrowing In on the Optimal Data Science Use Case 311 Chapter 17: Planning for Future Data Science Project Success 327 Chapter 18: Blazing a Path to Data Science Career Success 341 Part 5: The Part of Tens 367 Chapter 19: Ten Phenomenal Resources for Open Data 369 Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications 381 Index 397

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Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more IntrodTrade ReviewPraise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Praise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Table of ContentsThere Is More to Assessing Risk Than Statistics. The Need for Causal, Explanatory Models in Risk Assessment. Measuring Uncertainty: The Inevitability of Subjectivity. The Basics of Probability. Bayes’ Theorem and Conditional Probability. From Bayes’ Theorem to Bayesian Networks. Defining the Structure of Bayesian Networks. Building and Eliciting Node Probability Tables. Numeric Variables and Continuous Distribution Functions. Hypothesis Testing and Confidence Intervals. Modeling Operational Risk. Systems Reliability Modeling. Bayes and the Law. Learning Bayesian Networks. Decision making, Influence Diagrams and Value of information. Bayesian networks in forensics. Using Bayesian networks to debunk bad statistics. Bayesian networks for football prediction. Appendix A: The Basics of Counting. Appendix B: The Algebra of Node Probability Tables. Appendix C: Junction Tree Algorithm. Appendix D: Dynamic Discretization. Appendix E: Statistical Distributions.

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Book SynopsisUsing data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player''s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real woTrade Review"This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the forward: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combined with software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the foreword: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University “The real strength of this book is that it is meant to be hands-on. As part of the text, the authors provide access to a custom-built package in R, along with an excellent pre-prepared data set (one full season’s worth of NBA box score and play-by-play data). The authors then guide the reader through many examples of building graphs and tables using their R package and data. The graphs are often intricate and visually detailed, but the text shows how to make them quickly, giving detailed instructions. I imagine that a reader looking to get into basketball analysis could find this book very exciting, because it provides a quick and easy entry point into conducting sophisticated analyses and making visually arresting graphs and figures. A reader can easily follow along and replicate everything that is done in the book. Or, what is more likely, the reader can skim through the text until they come to a plot that looks particularly cool, and then by reading the surrounding section they can quickly learn how to do such an analysis for themselves.” –Brian Skinner, MIT "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combinedwith software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "For those interested in any level of statistical data analysis in basketball, specifically in R, Basketball Data Science: With Applications in R would be a valuable addition to their library. Further, this text would be quite useful for a course in sports data focusing on basketball or for a student’s research project." Russ Goodman, Central College, Iowa, USA, Mathematical Association of America, April 2023. Table of Contents1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.

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Book SynopsisFrank M. Carrano is Professor Emeritus of Computer Science at the University of Rhode Island. He received his Ph.D. degree in Computer Science from Syracuse University in 1969. His interests include data structures, computer science education, social issues in computing, and numerical computation. Professor Carrano is particularly interested in the design and delivery of undergraduate courses in computer science. He has authored several well-known computer science textbooks for undergraduates. Timothy M. Henry has a Bachelor of Science Degree in Mathematics from the U.S. Coast Guard Academy, a Master of Science Degree in Computer Science from Old Dominion University, and was awarded a PhD in Applied Math Sciences from the University of Rhode Island. He began his IT career as an officer in the U.S. Coast Guard, and among his early tours, he was the Information Resources Manager (what is today a CIO) at the Coast Guard's training centre in Yorktown, VA.Table of Contents Introduction Chapter 1: Bags Chapter 2: Bag Implementations That Use Arrays Chapter 3: A Bag Implementation That Links Data Chapter 4: The Effciency of Algorithms Chapter 5: Stacks Chapter 6: Stack Implementations Chapter 7: Recursion Chapter 8: An Introduction to Sorting Chapter 9: Faster Sorting Methods Chapter 10: Queues, Deques, and Priority Queues Chapter 11: Queue, Deque, and Priority Queue Implementations Chapter 12: Lists Chapter 13: A List Implementation That Uses an Array Chapter 14: A List Implementation That Links Data Chapter 15: Iterators for the ADT List Chapter 16: Sorted Lists Chapter 17: Inheritance and Lists Chapter 18: Searching Chapter 19: Dictionaries Chapter 20: Dictionary Implementations Chapter 21: Introducing Hashing Chapter 22: Hashing as a Dictionary Implementation Chapter 23: Trees Chapter 25: A Binary Search Tree Implementation Chapter 26: A Heap Implementation Chapter 27: Balanced Search Trees Chapter 28: Graphs Chapter 29: Graph Implementations

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Book SynopsisThis book describes the creation of an actual generic open source big data stack, which is an integrated stack of big data components--each of which serves a specific function like storage, resource management, or queueing. Each component has a big data heritage and community to support it. It can support big data in that it is able to scale, and it is a distributed and robust system.In the Complete Guide to Open Source Big Data Stack, Mike Frampton begins by creating a private cloud and then by installing and examining Apache Brooklyn. After that he will use each chapter to introduce one piece of the big data stacksharing how to source the software and then how to install it. He will then show how it works by simple example. Step by step and chapter by chapter, Frampton will create a real big data stack. The goal of this book is to show how a big data stack might be created and what components might be used. It attempts to do this with currently available ApaTable of ContentsChapter 1: The Big Data Stack Overview.- Chapter 2: Cloud Storage.- Chapter 3: Apache Brooklyn.- Chapter 4: Apache Mesos.- Chapter 5: Stack Storage Options.- Chapter 6: Processing.- Chapter 7: Streaming.- Chapter 8: Frameworks.- Chapter 9: Visualization.- Chapter 10: The Big Data Stack.-

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Book SynopsisLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You''ll Learn Become fluent in the essential concepts and terminology of data science and data engineering  Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling ofTable of Contents

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Book SynopsisStart developing with Oracle SQL. This book is a one-stop introduction to everything you need to know about getting started developing an Oracle Database. You''ll learn about foundational concepts, setting up a simple schema, adding data, reading data from the database, and making changes. No experience with databases is required to get started. Examples in the book are built around Oracle Live SQL, a freely available, online sandbox for practicing and experimenting with SQL statements, and Oracle Express Edition, a free version of Oracle Database that is available for download.A marquee feature of Beginning Oracle SQL for Oracle Database 18c is the small chapter size. Content is divided into easily digestible chunks that can be read and practiced in very short intervals of time, making this the ideal book for a busy professional to learn from. Even just a 15-20 minute block of free time can be put to good use.AuthoTable of ContentsIntroductionPart I. Setting Up1. What Is A Database?2. Setting UpPart II. Viewing Data3. Retrieving Data4. Selecting Specific Columns5. Restricting the Results6. Comparing Data7. Applying Multiple Filters8. Working with NULLs9. Removing Duplicate Results10. Applying Filters on Lists and Ranges of Values11. Ordering Your Data12. Applying Table and Column AliasesPart III. Adding, Updating, Deleting Data13. Understanding the Data Types14. Creating A Table15. Adding Data to A Table16. Updating and Removing Data17. Updating or Deleting A TablePart IV. Joining Tables18. Inner Join19. Outer Join20. Other Join Types21. Joining Many Tables28. Understanding the Alternative Join SyntaxPart V. Functions22. Using Functions in SQL23. Writing Conditional Logic24. Understanding Aggregate Functions25. Grouping Your Results 26. What Are Indexes?Part VI. Command Line27. Using the Command LinePart VII. Appendixes28. Appendix. How to Find and Navigate the Oracle SQL Reference

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Book SynopsisThere is no denying that the 21st century is data driven, with many digital industries relying on careful collection and analysis of mass volumes of information. A Chief Data Officer (CDO) at a company is the leader of this process, making the position an often daunting one. The Chief Data Officer Management Handbook is here to help. With this book, author Martin Treder advises CDOs on how to be better prepared for their swath of responsibilities, how to develop a more sustainable approach, and how to avoid the typical pitfalls. Based on positive and negative experiences shared by current CDOs, The Chief Data Officer Management Handbook guides you in designing the ideal structure of a data office, implementing it, and getting the right people on board. Important topics such as the data supply chain, data strategy, and data governance are thoughtfully covered by Treder. As a CDO it is important to use your position effectively with your entire team. The Chief Data Officer ManagementTable of Contents

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Book SynopsisGet started with blockchain development with this step-by-step guide. This book takes you all the way from installing requisite software through writing, testing, and deploying smart contracts. Getting Started with Ethereum delves into technologies most closely associated with Ethereum, such as IPFS, Filecoin, ENS, Chainlink, Truffle, Ganache, OpenZeppelin, Pinata, Fleek, Infura, Metamask, and Opensea. Author Davi Bauer walks you through project creation, how to compile projects and contracts, configure networks, and deploy smart contracts on blockchains. He then covers smart contracts, including deploying and verifying them. This book approaches blockchain in a way that allows you to focus on the topic that most interests you, covering Ethereum-related technologies broadly and not just focusing on Solidity.This hands-on guide offers a practical rather than conceptual approach get you coding. Upon completing this book, you Table of Contents● Pre requirements ○ Install Blockchain Dev Kit Extension on VS Code ■ Installing the extension ○ Install Truffle ■ Installing Truffle ■ Checking Truffle installation ○ Install Ganache CLI ■ Installing Ganache ■ Starting Ganache locally ○ Install Docker Chapter 1: MetaMask ○ Install and Setup MetaMask Wallet ■ Installing the wallet ■ Configuring the wallet ■ Accessing your wallet ■ Discovering your wallet address Chapter 2: Infura ○ Create an account on Infura ■ Creating a new account ■ Setting up your Infura project Chapter 3: Solidity ○ Get started with Solidity project on VS Code ■ Creating a new project ■ Compiling the project ■ Deploying to development Blockchain Chapter 4: ERC20 Tokens ○ Write a simple ERC20 token using OpenZeppelin ■ Preparing the environment ■ Writing the contract ■ Setting the Solidity compiler version ■ Compiling the contract ■ Verifying the result ○ Deploy ERC20 token to ganache development Blockchain ■ Preparing the migration ■ Writing the contract ■ Starting the Blockchain ■ Configuring the Blockchain network ■ Deploying the contract ■ Adding the token to a wallet ○ Create an ERC20 token with fixed supply ■ Creating the project ■ Writing the contract ■ Starting Ganache development Blockchain ■ Migrating the contract ■ Configuring MetaMask ■ Adding the token ■ Transferring tokens between accounts ○ Deploy ERC20 token to Testnet using Infura ■ Installing the pre-requirements ■ Setting up your Infura project ■ Setting up your Smart Contract ■ Configuring the private key ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on Etherscan ○ Deploy ERC20 token to Polygon Testnet (Layer 2) ■ Installing the pre-requirements ■ Adding Polygon Mumbai to MetaMask networks ■ Activating the Polygon add-on on Infura ■ Setting up your Infura project ■ Setting up your Smart Contract ■ Configuring the network (using Matic endpoint) ■ Configuring the network (using Infura endpoint) ■ Configuring the private key ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on Polygan Scan ○ Deploy ERC20 Token to Polygon Mainnet (Layer 2) ■ Adding Polygon Mainnet to MetaMask networks ■ Configuring the network (using Infura endpoint) ■ Deploying the Smart Contract ■ Checking your wallet balance ■ Verifying the Smart Contract on polyganscan Chapter 5: Unit Tests for Smart Contracts ○ Write Unit Tests for ERC20 Smart Contracts ■ Creating a new unit test file ■ Writing test for the contract total supply ■ Writing test asserting for the contract balance Chapter 6: ERC721- Non-Fungible Tokens ○ Create your art NFT using Ganache and OpenZeppelin ■ Creating the project ■ Configuring the wallet ■ Configuring the network ■ Configuring the solidity compiler ■ Configuring the private key ■ Creating the badge image ■ Adding the badge to your local IPFS ■ Pinning the badge to a remote IPFS node ■ Creating the badge metadata ■ Compiling the Smart Contract ■ Migrating the Smart Contract ■ Instantiate the Smart Contract ■ Awarding badge to a wallet ■ Checking badge on Etherscan ■ Adding the NFT token to your wallet ○ Sell your art NFT on Opensea ■ Connecting to OpenSea ■ Viewing your badge ■ Listing your badge for sale ■ Exploring listing details Chapter 7: Faucets ○ Get Test Ether From Faucet on Ropsten Network ■ Accessing the faucet ■ Waiting for the transaction ○ Get Test Ether From Faucet on Rinkeby Testnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet ○ Get Test MATIC From Faucet on Mumbai Testnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet ○ Get Test MATIC From Faucet on Mainnet ■ Preparing for funding ■ Funding your wallet ■ Checking your wallet Chapter 8: IPFS - InterPlanetary File System ○ Create Your IPFS Node ■ Installing the node ■ Configuring the node ■ Testing the node ■ Exploring your IPFS node ○ Add Files to IPFS ■ Adding the file ■ Viewing the file content on the console ■ Checking the file in the web ui ■ Viewing the file content in the browser ○ Setup IPFS Browser Extension ■ Installing the browser extension ■ Configuring the node type ■ Starting an external node ■ Importing a file ○ Pin and Unpin IPFS Files on Local Node ■ Starting your local node ■ Adding file to your node ■ Checking the file was added ■ Verifying your file was pinned ■ Unpinning your file ■ Pinning your file manually ○ Pin and Unpin Files on Remote Node using Pinata ■ Setting up API Keys on Pinata ■ Setting up Pinata as a remove service on your terminal ■ Adding a new file to your local IPFS node ■ Pinning your file to the remote IPFS node ■ Unpinning your file from the remote IPFS node ○ Host Your Site on IPFS Using Fleek ■ Login on Fleek ■ Cloning your existing repository ■ Installing Fleek ■ Initializing Fleek ■ Deploying your site Chapter 9: Filecoin ○ How to preserve files on Filecoin local node ■ Creating the project ■ Configuring truffle ■ Adding an image to be preserved ■ Installing dependencies ■ Starting local endpoints ■ Preserving files to Filecoin Chapter 10: ENS - Ethereum Name Service ○ Register your ENS to Receive any Crypto, Token or NFT on Your Wallet ■ Searching your domain name ■ Request to register ■ Managing your registration name ■ Checking the name resolution Chapter 11: Chainlink ○ Get Crypto Prices Inside Smart Contracts using Chainlink Oracles ■ Creating the project ■ Creating the Smart Contract ■ Creating the migration ■ Setting up your Infura project ■ Configuring the wallet ■ Configuring the network ■ Configuring the solidity compiler ■ Configuring the private key ■ Compiling the Smart Contract ■ Deploying the Smart Contract ■ Getting the price information from the Smart Contract Chapter 12: Nethereum ○ Get Ether Balance using Nethereum ■ Creating the project ■ Installing web3 ■ Creating the method ■ Getting the balance

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Book SynopsisCrypto is going to change the world, and for those tired of confusing financial jargon and complicated technical terminology, look no further. This book demystifies the world of cryptocurrencies and blockchain technology and explains in accessible language how it will affect your daily life. In The Quiet Crypto Revolution, Klaas Jung dives beneath the surface of Bitcoin to explore the engine that powers it - blockchain. Far surpassing the confines of cryptocurrencies, blockchain's potential for wide-ranging applications is enormous. It's crucial to understand that cryptocurrencies are merely a single manifestation of blockchain's capabilities. This book casts light on the broader spectrum of blockchain applications and the exciting future of this groundbreaking technology. With a focus on real-world applications, you'll gain a deeper understanding of the key concepts behind the innovative technology of blockchain, equipping you to make informed decisions. Whether you're a tech-savvy iTable of Contents1. Introduction to The Crypto Revolution.- 2. Understanding the Blockchain.- 3. The future of blockchain technology.- 4. Cryptocurrency in Practice.- 5. The Future of Decentralized Finance.- 6. Security and Scams.- 7. Crypto Pioneers: Exploring Entrepreneurial Opportunities.- 8. Final Thoughts: The Future of Crypto.

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Book SynopsisIs the Brexit vote successful big data politics or the end of democracy? Why do airlines overbook, and why do banks get it wrong so often? How does big data enable Netflix to forecast a hit, CERN to find the Higgs boson and medics to discover if red wine really is good for you? And how are companies using big data to benefit from smart meters, use advertising that spies on you and develop the gig economy, where workers are managed by the whim of an algorithm?The volumes of data we now access can give unparalleled abilities to make predictions, respond to customer demand and solve problems. But Big Brother's shadow hovers over it. Though big data can set us free and enhance our lives, it has the potential to create an underclass and a totalitarian state. With big data ever-present, you can't afford to ignore it. Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) - brings big data to life.Trade ReviewAs always, Clegg writes with an easy clarity that draws us in - no technical expertise required to understand his exploration of this essential subject - and throughout Big Data's highly enjoyable pages, the spread and range of material is highly impressive - dizzying in fact. I personally found entirely new perspectives on the subject that will keep me pondering for quite some time. I should add that, if I were still a statistics lecturer at Oxford, I would recommend the book to my students as bedside reading. -- Peet Morris * Former Lecturer in Statistics (St Hilda’s College Oxford), Lecturer/Researcher in software development *Clegg provides an engaging insight, reflecting on its positives and negatives. A holiday workout for the brain. * Saga Magazine *Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) brings big data to life. * Laboratory News *

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Book SynopsisThis book is intended for specialists in systems engineering interested in new, general techniques and for students and practitioners interested in using these techniques for solving specific practical problems. For many real-world, complex systems, it is possible to create easy-to-compute explicit analytical models instead of time-consuming computer simulations. Usually, however, analytical models are designed on a case-by-case basis, and there is a scarcity of general techniques for designing such easy-to-compute models. This book fills this gap by providing general recommendations for using analytical techniques in all stages of system design, implementation, testing, and monitoring. It also illustrates these recommendations using applications in various domains, such as more traditional engineering systems, biological systems (e.g., systems for cattle management), and medical and social-related systems (e.g., recommender systems).Table of Contents​Formulation of the Problem.- Analytical Techniques for Describing User Preferences: 80/20 Rule Partially Explains 7 Plus Minus 2 Law.- Analytical Techniques Help Enhance the Results of Data Mining: Case Study of Cow Insemination.- Case When Analytical Techniques Invalidate the Conclusions of Data Mining: Reversed Flynn Effect of Decreasing IQ Test Scores.- Analytical Techniques for Taking into Account Several Aspects of a Designed Systems: Case Study of Computation-Communication Tradeoff.- Analytical Techniques for Testing: Optimal Distribution of Testing Resources Between Different System Levels.- Index.

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Book SynopsisThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given.A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well.Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.Table of Contents1 Introduction to Neuromorphic Computing Systems.- 2 Neuromorphic System Design Fundamentals.- 3 Learning in Neuromorphic Systems.- 4 Emerging Memory Devices for Neuromorphic Systems.- 5 Communication Networks for Neuromorphic Systems.- 6 Fault-Tolerant Neuromorphic System Design.- 7 Reconfigurable Neuromorphic Computing System.- 8 Case Study: Real Hardware-Software Design of 3D-NoC-based Neuromorphic System.- 9 Survey of Neuromorphic Systems.

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Book SynopsisThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. Table of Contents1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow

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Book SynopsisThis book details recent developments in the emerging area of plug-and-play (PnP) visual subgraph query interfaces (VQI). These PnP interfaces are grounded in the principles of human-computer interaction (HCI) and cognitive psychology to address long-standing limitations to bottom-up search capabilities in graph databases using traditional graph query languages, which often require domain experts and specialist programmers. This book explains how PnP interfaces go against the traditional mantra of VQI construction by taking a data-driven approach and giving end users the freedom to easily and quickly construct and maintain a VQI for any data sources without resorting to coding. The book walks readers through the intuitive PnP interface that uses templates where the underlying graph repository represents the socket and user-specified requirements represent the plug. Hence, a PnP interface enables an end user to change the socket (i.e., graph repository) or the plug (i.e., requirements) as necessary to automatically and effortlessly generate VQIs. The book argues that such a data-driven paradigm creates several benefits, including superior support for visual subgraph query construction, significant reduction in the manual cost of constructing and maintaining a VQI for any graph data source, and portability of the interface across diverse sources and querying applications. This book provides a comprehensive introduction to the notion of PnP interfaces, compares it to its classical manual counterpart, and reviews techniques for automatic construction and maintenance of these new interfaces. In synthesizing current research on plug-and-play visual subgraph query interface management, this book gives readers a snapshot of the state of the art in this topic as well as future research directions.Table of ContentsChapter 1 - The Future is Democratized Graphs.- Chapter 2 - Background.- Chapter 3 - The World of Visual Graph Query Interfaces: An Overview.- Chapter 4 - Plug-and-Play Visual Subgraph Query Interfaces.- Chapter 5 - The Building Block of PnP Interfaces: Canned Patterns.- Chapter 6 - Pattern Selection for Graph Databases.- Chapter 7 - Pattern Selection for Large Networks.- Chapter 8 - Maintenance of Patterns.- Chapter 9 - The Road Ahead.

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Book SynopsisThis textbook is aimed at readers who have little or no knowledge of computer programming but want to learn to program in Python. It starts from the very basics including how to install your Python environment, how to write a very simple program and run it, what a variable is, what an if statement is, how iteration works using for and while loops as well as important key concepts such as functions, classes and modules. Each subject area is prefaced with an introductory chapter, before continuing with how these ideas work in Python. The second edition has been completely updated for the latest versions of Python including Python 3.11 and Python 3.12. New chapters have been added such as those that consider where and how Python is used, the use of Frozensets, how data can be sorted, enumerated types in Python, structural pattern matching and how (and why) Python Virtual Environments are configured. A new chapter ‘The Python Bites back’ is introduced to present the fourteen most common / biggest gotchas for someone new to Python. Other sections have been updated with new features such as Exception Groups, string operations and dictionary operations. A Beginners Guide to Python 3 Programming second Edition provides all you need to know about Python, with numerous examples provided throughout including several larger worked case studies illustrating the ideas presented in the previous chapters.Table of ContentsIntroduction.- Where is Python Used.- Setting up the Python Environment.- A First Python Program.- Python Strings.- Numbers, Booleans and None.- Flow of Control using if statements.- Number Guessing Game.- Recursion.- Introduction to Structured Analysis.- Functions in Python.- Implementing a Calculator using Functions.- Introduction to Functional Programming.- Curried Functions.- Introduction to Object Orientation.- Class Side and Static Behaviour.- Why Bother with Object Orientation?.- Operator Overloading.- Error and Exception Handling.- Python Modules and Packages.- Abstract Base Classes.- Error and Exception Handling.- Python Modules and Packages.- Protocols, Polymorphism and Descriptors.- Decorators.- Iterables and Iterators.- Generators and Coroutines.- Collections Tuples and Lists.- Sets.- Dictionaries.- Frozensets.- Collection Related Modules.- ADTs, Queues and Stacks.- Map, Filter and Reduce.- Sorting and Higher Order Functions.- Python Enumerated Values, Structural Pattern Making.- Python Virtual Environments.- Monkey Patching.- Attribute Lookup.- The Python Bites Back.- TicTacToe Game.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisThis book analyzes in detail the technological evolution process of databases in the era of cloud computing and explains how traditional database technology has gradually developed to cloud-native form from multiple perspectives such as architecture design, implementation mechanism, and system optimization.

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Book SynopsisThis book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will LearnFoundational principles of Lakehouse architectureKey features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live TablesDatabricks Intelligence Platform and key functionalitiesBuilding and deploying GenAI Applications from data ingestion to model servingDatabricks pricing, platform security, DBRX, and many more topicsWho This Book Is ForSolution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.

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Book SynopsisTable of ContentsChapter 1 Introduction 1.1 Opinions, Products 1.2 Roadmap to the Book 1.3 Terminology 1.4 Notation 1.5 Cryptographically Protected Sessions 1.6 Active and Passive Attacks 1.7 Legal Issues 1.7.1 Patents 1.7.2 Government Regulations 1.8 Some Network Basics 1.8.1 Network Layers 1.8.2 TCP and UDP Ports 1.8.3 DNS (Domain Name System) 1.8.4 HTTP and URLs 1.8.5 Web Cookies 1.9 Names for Humans 1.10 Authentication and Authorization 1.10.1 ACL (Access Control List) 1.10.2 Central Administration/Capabilities 1.10.3 Groups 1.10.4 Cross-Organizational and Nested Groups 1.10.5 Roles 1.11 Malware: Viruses, Worms, Trojan Horses 1.11.1 Where Does Malware Come From? 1.11.2 Virus Checkers 1.12 Security Gateway 1.12.1 Firewall 1.12.2 Application-Level Gateway/Proxy 1.12.3 Secure Tunnels 1.12.4 Why Firewalls Don't Work 1.13 Denial-of-Service (DoS) Attacks 1.14 NAT (Network Address Translation) 1.14.1 Summary Chapter 2 Introduction to Cryptography 2.1 Introduction 2.1.1 The Fundamental Tenet of Cryptography 2.1.2 Keys 2.1.3 Computational Difficulty 2.1.4 To Publish or Not to Publish 2.1.5 Earliest Encryption 2.1.6 One-Time Pad (OTP) 2.2 Secret Key Cryptography 2.2.1 Transmitting Over an Insecure Channel 2.2.2 Secure Storage on Insecure Media 2.2.3 Authentication 2.2.4 Integrity Check 2.3 Public Key Cryptography 2.3.1 Transmitting Over an Insecure Channel 2.3.2 Secure Storage on Insecure Media 2.3.3 Authentication 2.3.4 Digital Signatures 2.4 Hash Algorithms 2.4.1 Password Hashing 2.4.2 Message Integrity 2.4.3 Message Fingerprint 2.4.4 Efficient Digital Signatures 2.5 Breaking an Encryption Scheme 2.5.1 Ciphertext Only 2.5.2 Known Plaintext 2.5.3 Chosen Plaintext 2.5.4 Chosen Ciphertext 2.5.5 Side-Channel Attacks 2.6 Random Numbers 2.6.1 Gathering Entropy 2.6.2 Generating Random Seeds 2.6.3 Calculating a Pseudorandom Stream from the Seed 2.6.4 Periodic Reseeding 2.6.5 Types of Random Numbers 2.6.6 Noteworthy Mistakes 2.7 Numbers 2.7.1 Finite Fields 2.7.2 Exponentiation 2.7.3 Avoiding a Side-Channel Attack 2.7.4 Types of Elements used in Cryptography 2.7.5 Euclidean Algorithm 2.7.6 Chinese Remainder Theorem 2.8 Homework Chapter 3 Secret Key Cryptography 3.1 Introduction 3.2 Generic Block Cipher Issues 3.2.1 Blocksize, Keysize 3.2.2 Completely General Mapping 3.2.3 Looking Random 3.3 Constructing a Practical Block Cipher 3.3.1 Per-Round Keys 3.3.2 S-boxes and Bit Shuffles 3.3.3 Feistel Ciphers 3.4 Choosing Constants 3.5 Data Encryption Standard (DES) 3.5.1 DES Overview 3.5.2 The Mangler Function 3.5.3 Undesirable Symmetries 3.5.4 What's So Special About DES? 3.6 3DES (Multiple Encryption DES) 3.6.1 How Many Encryptions? 3.6.1.1 Encrypting Twice with the Same Key 3.6.1.2 Encrypting Twice with Two Keys 3.6.1.3 Triple Encryption with Only Two Keys 3.6.2 Why EDE Rather Than EEE? 3.7 Advanced Encryption Standard (AES) 3.7.1 Origins of AES 3.7.2 Broad Overview 3.7.3 AES Overview 3.7.4 Key Expansion 3.7.5 Inverse Rounds 3.7.6 Software Implementations of AES 3.8 RC4 3.9 Homework Chapter 4 Modes of Operation 4.1 Introduction 4.2 Encrypting a Large Message 4.2.1 ECB (Electronic Code Book) 4.2.2 CBC (Cipher Block Chaining) 4.2.2.1 Randomized ECB 4.2.2.2 CBC 4.2.2.3 CBC Threat—Modifying Ciphertext Blocks 4.2.3 CTR (Counter Mode) 4.2.3.1 Choosing IVs for CTR Mode 4.2.4 XEX (XOR Encrypt XOR) 4.2.5 XTS (XEX with Ciphertext Stealing) 4.3 Generating MACs 4.3.1 CBC-MAC 4.3.1.1 CBC Forgery Attack 4.3.2 CMAC 4.3.3 GMAC 4.3.3.1 GHASH 4.3.3.2 Transforming GHASH into GMAC 4.4 Ensuring Privacy and Integrity Together 4.4.1 CCM (Counter with CBC-MAC) 4.4.2 GCM (Galois/Counter Mode) 4.5 Performance Issues 4.6 Homework Chapter 5 Cryptographic Hashes 5.1 Introduction 5.2 The Birthday Problem 5.3 A Brief History of Hash Functions 5.4 Nifty Things to Do with a Hash 5.4.1 Digital Signatures 5.4.2 Password Database 5.4.3 Secure Shorthand of Larger Piece of Data 5.4.4 Hash Chains 5.4.5 Blockchain 5.4.6 Puzzles 5.4.7 Bit Commitment 5.4.8 Hash Trees 5.4.9 Authentication 5.4.10 Computing a MAC with a Hash 5.4.11 HMAC 5.4.12 Encryption with a Secret and a Hash Algorithm 5.5 Creating a Hash Using a Block Cipher 5.6 Construction of Hash Functions 5.6.1 Construction of MD4, MD5, SHA-1 and SHA-2 5.6.2 Construction of SHA-3 5.7 Padding 5.7.1 MD4, MD5, SHA-1, and SHA2-256 Message Padding 5.7.2 SHA-3 Padding Rule 5.8 The Internal Encryption Algorithms 5.8.1 SHA-1 Internal Encryption Algorithm 5.8.2 SHA-2 Internal Encryption Algorithm 5.9 SHA-3 f Function (Also Known as KECCAK-f) 5.10 Homework Chapter 6 First-Generation Public Key Algorithms 6.1 Introduction 6.2 Modular Arithmetic 6.2.1 Modular Addition 6.2.2 Modular Multiplication 6.2.3 Modular Exponentiation 6.2.4 Fermat's Theorem and Euler's Theorem 6.3 RSA 6.3.1 RSA Algorithm 6.3.2 Why Does RSA Work? 6.3.3 Why Is RSA Secure? 6.3.4 How Efficient Are the RSA Operations? 6.3.4.1 Exponentiating with Big Numbers 6.3.4.2 Generating RSA Keys 6.3.4.3 Why a Non-Prime Has Multiple Square Roots of One 6.3.4.4 Having a Small Constant e 6.3.4.5 Optimizing RSA Private Key Operations 6.3.5 Arcane RSA Threats 6.3.5.1 Smooth Numbers 6.3.5.2 The Cube Root Problem 6.3.6 Public-Key Cryptography Standard (PKCS) 6.3.6.1 Encryption 6.3.6.2 The Million-Message Attack 6.3.6.3 Signing 6.4 Diffie-Hellman 6.4.1 MITM (Meddler-in-the-Middle) Attack 6.4.2 Defenses Against MITM Attack 6.4.3 Safe Primes and the Small-Subgroup Attack 6.4.4 ElGamal Signatures 6.5 Digital Signature Algorithm (DSA) 6.5.1 The DSA Algorithm 6.5.2 Why Is This Secure? 6.5.3 Per-Message Secret Number 6.6 How Secure Are RSA and Diffie-Hellman? 6.7 Elliptic Curve Cryptography (ECC) 6.7.1 Elliptic Curve Diffie-Hellman (ECDH) 6.7.2 Elliptic Curve Digital Signature Algorithm (ECDSA) 6.8 Homework Chapter 7 Quantum Computing 7.1 What Is a Quantum Computer? 7.1.1 A Preview of the Conclusions 7.1.2 First, What Is a Classical Computer? 7.1.3 Qubits and Superposition 7.1.3.1 Example of a Qubit 7.1.3.2 Multi-Qubit States and Entanglement 7.1.4 States and Gates as Vectors and Matrices 7.1.5 Becoming Superposed and Entangled 7.1.6 Linearity 7.1.6.1 No Cloning Theorem 7.1.7 Operating on Entangled Qubits 7.1.8 Unitarity 7.1.9 Doing Irreversible Operations by Measurement 7.1.10 Making Irreversible Classical Operations Reversible 7.1.11 Universal Gate Sets 7.2 Grover's Algorithm 7.2.1 Geometric Description 7.2.2 How to Negate the Amplitude of |k⟩ 7.2.3 How to Reflect All the Amplitudes Across the Mean 7.2.4 Parallelizing Grover's Algorithm 7.3 Shor's Algorithm 7.3.1 Why Exponentiation mod n Is a Periodic Function 7.3.2 How Finding the Period of ax mod n Lets You Factor n 7.3.3 Overview of Shor's Algorithm 7.3.4 Converting to the Frequency Graph—Introduction 7.3.5 The Mechanics of Converting to the Frequency Graph 7.3.6 Calculating the Period 7.3.7 Quantum Fourier Transform 7.4 Quantum Key Distribution (QKD) 7.4.1 Why It's Sometimes Called Quantum Encryption 7.4.2 Is Quantum Key Distribution Important? 7.5 How Hard Are Quantum Computers to Build? 7.6 Quantum Error Correction 7.7 Homework Chapter 8 Post-Quantum Cryptography 8.1 Signature and/or Encryption Schemes 8.1.1 NIST Criteria for Security Levels 8.1.2 Authentication 8.1.3 Defense Against Dishonest Ciphertext 8.2 Hash-based Signatures 8.2.1 Simplest Scheme – Signing a Single Bit 8.2.2 Signing an Arbitrary-sized Message 8.2.3 Signing Lots of Messages 8.2.4 Deterministic Tree Generation 8.2.5 Short Hashes 8.2.6 Hash Chains 8.2.7 Standardized Schemes 8.2.7.1 Stateless Schemes 8.3 Lattice-Based Cryptography 8.3.1 A Lattice Problem 8.3.2 Optimization: Matrices with Structure 8.3.3 NTRU-Encryption Family of Lattice Encryption Schemes 8.3.3.1 Bob Computes a (Public, Private) Key Pair 8.3.3.2 How Bob Decrypts to Find m 8.3.3.3 How Does this Relate to Lattices? 8.3.4 Lattice-Based Signatures 8.3.4.1 Basic Idea 8.3.4.2 Insecure Scheme 8.3.4.3 Fixing the Scheme 8.3.5 Learning with Errors (LWE) 8.3.5.1 LWE Optimizations 8.3.5.2 LWE-based NIST Submissions 8.4 Code-based Schemes 8.4.1 Non-cryptographic Error-correcting Codes 8.4.1.1 Invention Step 8.4.1.2 Codeword Creation Step 8.4.1.3 Misfortune Step 8.4.1.4 Diagnosis Step 8.4.2 The Parity-Check Matrix 8.4.3 Cryptographic Public Key Code-based Scheme 8.4.3.1 Neiderreiter Optimization 8.4.3.2 Generating a Public Key Pair 8.4.3.3 Using Circulant Matrices 8.5 Multivariate Cryptography 8.5.1 Solving Linear Equations 8.5.2 Quadratic Polynomials 8.5.3 Polynomial Systems 8.5.4 Multivariate Signature Systems 8.5.4.1 Multivariate Public Key Signatures 8.6 Homework Chapter 9 Authentication of People 9.1 Password-based Authentication 9.1.1 Challenge-Response Based on Password 9.1.2 Verifying Passwords 9.2 Address-based Authentication 9.2.1 Network Address Impersonation 9.3 Biometrics 9.4 Cryptographic Authentication Protocols 9.5 Who Is Being Authenticated? 9.6 Passwords as Cryptographic Keys 9.7 On-Line Password Guessing 9.8 Off-Line Password Guessing 9.9 Using the Same Password in Multiple Places 9.10 Requiring Frequent Password Changes 9.11 Tricking Users into Divulging Passwords 9.12 Lamport's Hash 9.13 Password Managers 9.14 Web Cookies 9.15 Identity Providers (IDPs) 9.16 Authentication Tokens 9.16.1 Disconnected Tokens 9.16.2 Public Key Tokens 9.17 Strong Password Protocols 9.17.1 Subtle Details 9.17.2 Augmented Strong Password Protocols 9.17.3 SRP (Secure Remote Password) 9.18 Credentials Download Protocols 9.19 Homework Chapter 10 Trusted Intermediaries 10.1 Introduction 10.2 Functional Comparison 10.3 Kerberos 10.3.1 KDC Introduces Alice to Bob 10.3.2 Alice Contacts Bob 10.3.3 Ticket Granting Ticket (TGT) 10.3.4 Interrealm Authentication 10.3.5 Making Password-Guessing Attacks Difficult 10.3.6 Double TGT Protocol 10.3.7 Authorization Information 10.3.8 Delegation 10.4 PKI 10.4.1 Some Terminology 10.4.2 Names in Certificates 10.5 Website Gets a DNS Name and Certificate 10.6 PKI Trust Models 10.6.1 Monopoly Model 10.6.2 Monopoly plus Registration Authorities (RAs) 10.6.3 Delegated CAs 10.6.4 Oligarchy 10.6.5 Anarchy Model 10.6.6 Name Constraints 10.6.7 Top-Down with Name Constraints 10.6.8 Multiple CAs for Any Namespace Node 10.6.9 Bottom-Up with Name Constraints 10.6.9.1 Functionality of Up-Links 10.6.9.2 Functionality of Cross-Links 10.6.10 Name Constraints in PKIX Certificates 10.7 Building Certificate Chains 10.8 Revocation 10.8.1 CRL (Certificate Revocation list 10.8.2 Online Certificate Status Protocol (OCSP) 10.8.3 Good-Lists vs. Bad-Lists 10.9 Other Information in a PKIX Certificate 10.10 Issues with Expired Certificates 10.11 DNSSEC (DNS Security Extensions) 10.12 Homework Chapter 11 Communication Session Establishment 11.1 One-way Authentication of Alice 11.1.1 Timestamps vs. Challenges 11.1.2 One-Way Authentication of Alice using a Public Key 11.2 Mutual Authentication 11.2.1 Reflection Attack 11.2.2 Timestamps for Mutual Authentication 11.3 Integrity/Encryption for Data 11.3.1 Session Key Based on Shared Secret Credentials 11.3.2 Session Key Based on Public Key Credentials 11.3.3 Session Key Based on One-Party Public Keys 11.4 Nonce Types 11.5 Intentional MITM 11.6 Detecting MITM 11.7 What Layer? 11.8 Perfect Forward Secrecy 11.9 Preventing Forged Source Addresses 11.9.1 Allowing Bob to Be Stateless in TCP 11.9.2 Allowing Bob to Be Stateless in IPsec 11.10 Endpoint Identifier Hiding 11.11 Live Partner Reassurance 11.12 Arranging for Parallel Computation 11.13 Session Resumption/Multiple Sessions 11.14 Plausible Deniability 11.15 Negotiating Crypto Parameters 11.15.1 Suites vs. à la Carte 11.15.2 Downgrade Attack 11.16 Homework Chapter 12 IPsec 12.1 IPsec Security Associations 12.1.1 Security Association Database 12.1.2 Security Policy Database 12.1.3 IKE-SAs and Child-SAs 12.2 IKE (Internet Key Exchange Protocol) 12.3 Creating a Child-SA 12.4 AH and ESP 12.4.1 ESP Integrity Protection 12.4.2 Why Protect the IP Header? 12.4.3 Tunnel, Transport Mode 12.4.4 IPv4 Header 12.4.5 IPv6 Header 12.5 AH (Authentication Header) 12.6 ESP (Encapsulating Security Payload) 12.7 Comparison of Encodings 12.8 Homework Chapter 13 SSL/TLS and SSH 13.1 Using TCP 13.2 StartTLS 13.3 Functions in the TLS Handshake 13.4 TLS 1.2 (and Earlier) Basic Protocol 13.5 TLS 1.3 13.6 Session Resumption 13.7 PKI as Deployed by TLS 13.8 SSH (Secure Shell) 13.8.1 SSH Authentication 13.8.2 SSH Port Forwarding 13.9 Homework Chapter 14 Electronic Mail Security 14.1 Distribution Lists 14.2 Store and Forward 14.3 Disguising Binary as Text 14.4 HTML-Formatted Email 14.5 Attachments 14.6 Non-cryptographic Security Features 14.6.1 Spam Defenses 14.7 Malicious Links in Email 14.8 Data Loss Prevention (DLP) 14.9 Knowing Bob's Email Address 14.10 Self-Destruct, Do-Not-Forward, 14.11 Preventing Spoofing of From Field 14.12 In-Flight Encryption 14.13 End-to-End Signed and Encrypted Email 14.14 Encryption by a Server 14.15 Message Integrity 14.16 Non-Repudiation 14.17 Plausible Deniability 14.18 Message Flow Confidentiality 14.19 Anonymity 14.20 Homework Chapter 15 Electronic Money 15.1 ECASH 15.2 Offline eCash 15.2.1 Practical Attacks 15.3 Bitcoin 15.3.1 Transactions 15.3.2 Bitcoin Addresses 15.3.3 Blockchain 15.3.4 The Ledger 15.3.5 Mining 15.3.6 Blockchain Forks 15.3.7 Why Is Bitcoin So Energy-Intensive? 15.3.8 Integrity Checks: Proof of Work vs. Digital Signatures 15.3.9 Concerns 15.4 Wallets for Electronic Currency 15.5 Homework Chapter 16 Cryptographic Tricks 16.1 Secret Sharing 16.2 Blind Signature 16.3 Blind Decryption 16.4 Zero-Knowledge Proofs 16.4.1 Graph Isomorphism ZKP 16.4.2 Proving Knowledge of a Square Root 16.4.3 Noninteractive ZKP 16.5 Group Signatures 16.5.1 Trivial Group Signature Schemes 16.5.1.1 Single Shared Key 16.5.1.2 Group Membership Certificate 16.5.1.3 Multiple Group Membership Certificates 16.5.1.4 Blindly Signed Multiple Group Membership Certificates 16.5.2 Ring Signatures 16.5.3 DAA (Direct Anonymous Attestation) 16.5.4 EPID (Enhanced Privacy ID) 16.6 Circuit Model 16.7 Secure Multiparty Computation (MPC) 16.8 Fully Homomorphic Encryption (FHE) 16.8.1 Bootstrapping 16.8.2 Easy-to-Understand Scheme 16.9 Homework Chapter 17 Folklore 17.1 Misconceptions 17.2 Perfect Forward Secrecy 17.3 Change Encryption Keys Periodically 17.4 Don't Encrypt without Integrity Protection 17.5 Multiplexing Flows over One Secure Session 17.5.1 The Splicing Attack 17.5.2 Service Classes 17.5.3 Different Cryptographic Algorithms 17.6 Using Different Secret Keys 17.6.1 For Initiator and Responder in Handshake 17.6.2 For Encryption and Integrity 17.6.3 In Each Direction of a Secure Session 17.7 Using Different Public Keys 17.7.1 Use Different Keys for Different Purposes 17.7.2 Different Keys for Signing and Encryption 17.8 Establishing Session Keys 17.8.1 Have Both Sides Contribute to the Master Key 17.8.2 Don't Let One Side Determine the Key 17.9 Hash in a Constant When Hashing a Password 17.10 HMAC Rather than Simple Keyed Hash 17.11 Key Derivation 17.12 Use of Nonces in Protocols 17.13 Creating an Unpredictable Nonce 17.14 Compression 17.15 Minimal vs. Redundant Designs 17.16 Overestimate the Size of Key 17.17 Hardware Random Number Generators 17.18 Put Checksums at the End of Data 17.19 Forward Compatibility 17.19.1 Options 17.19.2 Version Numbers 17.19.2.1 Version Number Field Must Not Move 17.19.2.2 Negotiating Highest Version Supported 17.19.2.3 Minor Version Number Field Glossary Math M.1 Introduction M.2 Some definitions and notation M.3 Arithmetic M.4 Abstract Algebra M.5 Modular Arithmetic M.5.1 How Do Computers Do Arithmetic? M.5.2 Computing Inverses in Modular Arithmetic M.5.2.1 The Euclidean Algorithm M.5.2.2 The Chinese Remainder Theorem M.5.3 How Fast Can We Do Arithmetic? M.6 Groups M.7 Fields M.7.1 Polynomials M.7.2 Finite Fields M.7.2.1 What Sizes Can Finite Fields Be? M.7.2.2 Representing a Field M.8 Mathematics of Rijndael M.8.1 A Rijndael Round M.9 Elliptic Curve Cryptography M.10 Rings M.11 Linear Transformations M.12 Matrix Arithmetic M.12.1 Permutations M.12.2 Matrix Inverses M.12.2.1 Gaussian Elimination M.13 Determinants M.13.1 Properties of Determinants M.13.1.1 Adjugate of a Matrix M.13.2 Proof: Determinant of Product is Product of Determinants M.14 Homework Bibliography 9780136643609 TOC 8/2/2022

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Book SynopsisPrepare for Microsoft Exam 70-761–and help demonstrate your real-world mastery of SQL Server 2016 Transact-SQL data management, queries, and database programming. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical-thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: • Filter, sort, join, aggregate, and modify data • Use subqueries, table expressions, grouping sets, and pivoting • Query temporal and non-relational data, and output XML or JSON • Create views, user-defined functions, and stored procedures • Implement error handling, transactions, data types, and nulls This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have experience working with SQL Server as a database administrator, system engineer, or developer • Includes downloadable sample database and code for SQL Server 2016 SP1 (or later) and Azure SQL Database Querying Data with Transact-SQL About the Exam Exam 70-761 focuses on the skills and knowledge necessary to manage and query data and to program databases with Transact-SQL in SQL Server 2016. About Microsoft Certification Passing this exam earns you credit toward a Microsoft Certified Solutions Associate (MCSA) certification that demonstrates your mastery of essential skills for building and implementing on-premises and cloud-based databases across organizations. Exam 70-762 (Developing SQL Databases) is also required for MCSA: SQL 2016 Database Development certification. See full details at: microsoft.com/learningTable of ContentsCHAPTER 1 Manage data with Transact-SQL CHAPTER 2 Query data with advanced Transact-SQL components CHAPTER 3 Program databases by using Transact-SQL

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Book SynopsisLarge datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark. Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book introduces Dask Data Frames and teaches helpful code patterns to streamline the reader’s analysis. Key Features Working with large structured datasets Writing DataFrames Cleaningand visualizing DataFrames Machine learning with Dask-ML Working with Bags and Arrays Written for data engineers and scientists with experience using Python. Knowledge of the PyData stack (Pandas, NumPy, and Scikit-learn) will be helpful. No experience with low-level parallelism is required. About the technology Dask is a self-contained, easily extendible library designed to query, stream, filter, and consolidate huge datasets. Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.

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Book SynopsisRevolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. Data Mesh in Action reveals how this ground breaking architecture looks for both small start-ups and large enterprises. You'll see a datamesh in action as you explore both an extended case study andmultiple real-world examples. As you go, you'll be expertly guidedthrough discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-productsystem.

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Book SynopsisTake a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX.While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it''s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Table of ContentsChapter 1: Show Me the Data Chapter 2: DAX Objects, Syntax & Formatting Chapter 3: Calculated Columns & Measures Chapter 4: Evaluation Context Chapter 5: Iterators Chapter 6: The CALCULATE Function Chapter 7: DAX Table Functions Chapter 8: The ALL Function and All its Variations Chapter 9: Calculations on Dates: Using DAX Time Intelligence Chapter 10: Empty Values Versus Zero Chapter 11: Using Variables: Making Our Code More Readable Chapter 12: Returning Values in the Current Filter Chapter 13: Controlling the Direction of Filter Propagation Chapter 14: Working with Multiple Relationships Between Tables Chapter 15: Understanding Context Transition Chapter 16: Leveraging Context Transition Chapter 17: Virtual Relationships: the LOOKUPVALUE and TREATAS Functions Chapter 18: Table Expansion Chapter 19: The CALCULATETABLE Function

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Book SynopsisFinancial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk.

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Book Synopsis Jon Krohn is the chief data scientist at untapt, a machine learning startup in New York. He leads a flourishing Deep Learning Study Group, presents the acclaimed Deep Learning with TensorFlow LiveLessons in Safari, and teaches his Deep Learning curriculum at the NYC Data Science Academy. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. Grant Beyleveld is a doctoral candidate at the Icahn School of Medicine at New York's Mount Sinai hospital, researching the relationship between viruses and their hosts. A founding member of the Deep Learning Study Group, he holds a masters in molecular medicine and medical biochemistry from the University of Witwatersrand. Aglaé Bassens is a Belgian artist based in Brooklyn. She studied fine arts at The Ruskin School of Drawing and Fine Art, Oxford University, and University College London's SlaTrade Review“Over the next few decades, artificial intelligence is poised to dramatically change almost every aspect of our lives, in large part due to today’s breakthroughs in deep learning. The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come.” —Tim Urban, writer and illustrator of Wait But Why “This book is an approachable, practical, and broad introduction to deep learning, and the most beautifully illustrated machine learning book on the market.” —Dr. Michael Osborne, Dyson Associate Professor in Machine Learning, University of Oxford “This book should be the first stop for deep learning beginners, as it contains lots of concrete, easy-to-follow examples with corresponding tutorial videos and code notebooks. Strongly recommended.” —Dr. Chong Li, cofounder, Nakamoto & Turing Labs; adjunct professor, Columbia University “It’s hard to imagine developing new products today without thinking about enriching them with capabilities using machine learning. Deep learning in particular has many practical applications, and this book’s intelligible clear and visual approach is helpful to anyone who would like to understand what deep learning is and how it could impact your business and life for years to come.” —Helen Altshuler, engineering leader, Google “This book leverages beautiful illustrations and amusing analogies to make the theory behind deep learning uniquely accessible. Its straightforward example code and best-practice tips empower readers to immediately apply the transformative technique to their particular niche of interest.” –Dr. Rasmus Rothe, founder, Merantix “This is an invaluable resource for anyone looking to understand what deep learning is and why it powers almost every automated application today, from chatbots and voice recognition tools to self-driving cars. The illustrations and biological explanations help bring to life a complex topic and make it easier to grasp fundamental concepts.” –Joshua March, CEO and cofounder, Conversocial; author of Message Me “Deep learning is regularly redefining the state of the art across machine vision, natural language, and sequential decision-making tasks. If you too would like to pass data through deep neural networks in order to build high-performance models, then this book–with its innovative, highly visual approach–is the ideal place to begin.” –Dr. Alex Flint, roboticist and entrepreneur Table of ContentsFigures xixTables xxviiExamples xxixForeword xxxiiiPreface xxxvAcknowledgments xxxixAbout the Authors xliPart I: Introducing Deep Learning 1Chapter 1: Biological and Machine Vision 3Biological Vision 3Machine Vision 8TensorFlow Playground 17Quick, Draw! 19Summary 19Chapter 2: Human and Machine Language 21Deep Learning for Natural LanguageProcessing 21Computational Representations of Language 25Elements of Natural Human Language 33Google Duplex 35Summary 37Chapter 3: Machine Art 39A Boozy All-Nighter 39Arithmetic on Fake Human Faces 41Style Transfer: Converting Photos into Monet (and Vice Versa) 44Make Your Own Sketches Photorealistic 45Creating Photorealistic Images from Text 45Image Processing Using Deep Learning 46Summary 48Chapter 4: Game-Playing Machines 49Deep Learning, AI, and Other Beasts 49Three Categories of Machine Learning Problems 53Deep Reinforcement Learning 56Video Games 57Board Games 59Manipulation of Objects 67Popular Deep Reinforcement Learning Environments 68Three Categories of AI 71Summary 72Part II: Essential Theory Illustrated 73Chapter 5: The (Code) Cart Ahead of the (Theory)Horse 75Prerequisites 75Installation 76A Shallow Network in Keras 76Summary 84Chapter 6: Artificial Neurons Detecting Hot Dogs 85Biological Neuroanatomy 101 85The Perceptron 86Modern Neurons and Activation Functions 91Choosing a Neuron 96Summary 96Key Concepts 97Chapter 7: Artificial Neural Networks 99The Input Layer 99Dense Layers 99A Hot Dog-Detecting Dense Network 101The Softmax Layer of a Fast Food-Classifying Network 106Revisiting Our Shallow Network 108Summary 110Key Concepts 110Chapter 8: Training Deep Networks 111Cost Functions 111Optimization: Learning to Minimize Cost 115Backpropagation 124Tuning Hidden-Layer Count and NeuronCount 125An Intermediate Net in Keras 127Summary 129Key Concepts 130Chapter 9: Improving Deep Networks 131Weight Initialization 131Unstable Gradients 137Model Generalization (Avoiding Overfitting) 140Fancy Optimizers 145A Deep Neural Network inKeras 147Regression 149TensorBoard 152Summary 154Key Concepts 155Part III: Interactive Applications of Deep Learning 157Chapter 10: Machine Vision 159Convolutional Neural Networks 159Pooling Layers 169LeNet-5 in Keras 171AlexNet and VGGNet in Keras 176Residual Networks 179Applications of Machine Vision 182Summary 193Key Concepts 193Chapter 11: Natural Language Processing 195Preprocessing Natural Language Data 195Creating Word Embeddings with word2vec 206The Area under the ROC Curve 217Natural Language Classification with Familiar Networks 222Networks Designed for Sequential Data 240Non-sequential Architectures: The Keras Functional API 251Summary 256Key Concepts 257Chapter 12: Generative Adversarial Networks 259Essential GAN Theory 259The Quick, Draw! Dataset 263The Discriminator Network 266The Generator Network 269The Adversarial Network 272GAN Training 275Summary 281Key Concepts 282Chapter 13: Deep Reinforcement Learning 283Essential Theory of Reinforcement Learning 283Essential Theory of Deep Q-Learning Networks 290Defining a DQN Agent 293Interacting with an OpenAI Gym Environment 300Hyperparameter Optimization with SLM Lab 303Agents Beyond DQN 306Summary 308Key Concepts 309Part IV: You and AI 311Chapter 14: Moving Forward with Your Own Deep Learning Projects 313Ideas for Deep Learning Projects 313Resources for Further Projects 317The Modeling Process, Including Hyperparameter Tuning 318Deep Learning Libraries 321Software 2.0 324Approaching Artificial General Intelligence 326Summary 328Part V: Appendices 331Appendix A: Formal Neural Network Notation 333Appendix B: Backpropagation 335Appendix C: PyTorch 339PyTorch Features 339PyTorch in Practice 341Index 345

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Book SynopsisThe Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.Trade Review"Pursuing educational AI along more ethical lines requires considerable time and effort, and a considerable amount of deliberation, debate, dialogue, and consensus building. All of this implies replacing ambitions of ‘scaling-up’ with a commitment to slowing-down. This book takes a great initial step in the right direction."—Neil Selwyn, Distinguished Professor in the Faculty of Education, Monash University, Australia, from his foreword"This book contributes importantly to inform and sensibilize readers towards encoding ethics in the AI used in education, at times challenging the status quo, as well as current pedagogical and technological practices."—Gabriela Ramos, Assistant Director-General for Social and Human Sciences, UNESCO, from her forewordTable of ContentsPart I: Ethics of AI In Education: An Outside Perspective 1. Learning to learn differently 2. Educational research and Artificial Intelligence in education: Identifying ethical challenges 3. AI in education: An opportunity riddled with challenges 4. Student-centered requirements for the ethics of AI in education 5. Pitfalls and pathways for trustworthy Artificial Intelligence in education Part II: Ethics of AI In Education: An Inside Perspective 6. Equity and Artificial Intelligence in education: Will “AIED” amplify or alleviate inequities in education? 7. Algorithmic fairness in education 8. Beyond “Fairness:” Structural (in)justice lenses on AI for education 9. The overlapping ethical imperatives of human teachers and their Artificially Intelligent assistants. 10. Integrating AI ethics across the computing curriculum

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Book SynopsisData science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerTable of ContentsSection I: Introduction about Data Science and Data Analytics 1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach 2. IoT Analytics/Data Science for IoT 3. A Model to Identify Agriculture Production Using Data Science Techniques 4. Identification and Classification of Paddy Crop Diseases Using Big Data Machine Learning Techniques Section II Algorithms, Methods, and Tools for Data Science and Data Analytics 5. Crop Models and Decision Support Systems Using Machine Learning 6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random Forest 7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning 8. Hybrid Cellular Automata Models for Discrete Dynamical Systems 9. An Efficient Imputation Strategy Based on Adaptive Filter for Large Missing Value Datasets 10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models Section III: Applications of Data Science and Data Analytics 11. Wheat Rust Disease Detection Using Deep Learning 12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary Disease 13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical LSTM Methods 14. A Tier-based Educational Analytics Framework 15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images 16. Prediction of Acoustic Performance Using Machine Learning Techniques Section IV: Issue and Challenges in Data Science and Data Analytics 17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony 18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian Information Technology Stocks 19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments 20. Churn Prediction in Banking the Sector 21. Machine and Deep Learning Techniques for Internet of Things Based Cloud Systems Section V: Future Research Opportunities towards Data Science and Data Analytics 22. Dialect Identification of the Bengali Language 23. Real-Time Security Using Computer Vision 24. Data Analytics for Detecting DDoS Attacks in Network Traffic 25. Detection of Patterns in Attributed Graph Using Graph Mining 26. Analysis and Prediction of the Update of Mobile Android Version

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Book SynopsisData analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of quesTable of Contents1 History and Evolution of Data Analytics 2 Data Mining and Analytics 3 Data Analytics Tools 4 Business Analytics and Intelligence 5 Customer Relationship Analytics, Cloud Computing, Blockchain, and Cognitive Computing 6 Cybersecurity and Data Analytics 7 Data Analytics and the Retail Industry 8 Data Analytics in the Financial Services Industry 9 Data Analytics in the Sports Industry 10 Data Analytics in the Accounting Industry 11 Data Analytics in the Medical Industry 12 Data Analytics in the Manufacturing Industry 13 Data Analytics in the Marketing Industry 14 Data Analytics in the Transportation Industry 15 Data Analytics in Education

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Book SynopsisThe world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced.IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentiTable of ContentsChapter 1. Artificial Intelligence. Chapter 2. Machine Learning. Chapter 3. The high Level Overview into Neural Networks. Chapter 4. Typical Applications for Computer Vision. Chapter 5. Conclusion.

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Book SynopsisData Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences.Key Features:â Elucidation of data scientific workflow. â Introduction to reproducible research. â In-depth coverage of data-scientifTable of Contents1. Bienvenue! 2. Getting Started 3. Why Data Science? 4. Data Manipulation 5. Data Visualization 6. Automated Reporting 7. Example Project: The Biscuit Study 8. Data Collection 9. Data Preparation 10. Data Analysis 11. Value Delivery 12. Machine Learning 13. Text Analysis 14. Dashboards 15. Conclusion and Next Steps

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Book SynopsisThe book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout. Trade Review"This clean and fun book covers a wide range of topics on statistical communication, programming, and modeling in a way that should be a useful supplement to any statistics course or self-learning program. I absolutely love this book!"- Andrew Gelman, Columbia University"An excellent book. Communication and reproducibility are of increasing concern in statistics, and this book covers these topics and more in a practical, appealing, and truly unique way."- Daniela Witten, University of Washington"Many data science texts tell you how to perform perfunctory calculations. Instead, Telling Stories with Data tells you how to engage in the mindset and process of analysis. By arming students with the computational, statistical and philosophical skills needed to use data in sense-making and story-telling, this book stands out from the pack as uniquely actionable and empowering."- Emily Riederer, Capital One"This is not another statistics book. It is much better than that. It is a book about doing quantitative research, about scientific justification, about quality control, about communication and epistemic humility. It's a valuable supplement to any methods curriculum, and useful for self-learners as well."- Richard McElreath, Max Planck Institute for Evolutionary Anthropology"Telling Stories with Data is a thoughtful guide to using data to learn and affect positive change. The book includes each stage of the process and can serve as a long-lasting companion to many data scientists and future data story tellers."- Christopher Peters, Zapier“A clever career choice is to pick a field where your skills are complementary with a growing resource. In the coming decades, those who are adept in analysing data will flourish. That means crunching statistics and telling compelling stories. Rohan Alexander’s book will help you do both.”- Andrew Leigh, Member of the Australian Parliament and author of Randomistas: How Radical Researchers Are Changing Our World"Every data analyst has to tell stories with data, and yet traditional textbooks focus on statistical methods alone. Telling Stories with Data teaches the entire data science workflow, including data acquisition, communication, and reproducibility. I highly recommend this unique book!"- Kosuke Imai, Harvard University"This is an extraordinary, wonderful, book, full of wise advice for anyone starting in data science. Intermixing concepts and code means the ideas are immediately made concrete, and the emphasis on reproducible workflows brings a welcome dose of rigor to a rapidly developing field."- David Spiegelhalter, The University of CambridgeTable of Contents1. Telling stories with data 2. Drinking from a fire hose 3. Reproducible workflows Part 1. Foundations 4. Writing research 5. Static communication Part 2. Communication 6. Farm data 7. Gather data 8. Hunt data Part 3. Acquisition 9. Clean and prepare 10. Store and share Part 4. Preparation 11. Exploratory data analysis 12. Linear models 13. Generalized linear models 14. Causality from observational data 15. Multilevel regression with post-stratification 16. Text as data 17. Concluding remarks

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Book SynopsisHow can we give data physical form?And how might those creations change the ways we experience data and the stories it can tell?Making with Data: Physical Design and Craft in a Data-Driven World provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. Across 25+ beautifully-illustrated chapters, international artists, designers, and scientists each explain the process of creating a specific data-driven pieceâillustrating their practice with candid sketches, photos, and design artifacts from their own studios.The author website, featuring updates and more information about the projects behind the book, can be found here: https://makingwithdata.org/.Featuring influential voices in computer science, data science, graphic design, art, craft, and architecture, Making with Data is accessible and inspiring for entTrade Review"A mind-blowing collection! With the rich visual process descriptions, the creators invite us into their workshops and let us look over their shoulders. You will discover both an exhibition of wonderful data-inspired works as well as the backstories of each of these pieces. Whether hand-made, machine-controlled, or through natural processes, all the chapters show fascinating and bespoke creations of data objects. A much needed collection highlighting what is happening at the frontiers of art and sciences in this new field of data design."-- Giorgia Lupi, partner at Pentagram and author of Dear Data"What a much-needed book! Till, Sam, Lora, and Wes show us that data communication can be so much more than just visualization. There is a whole exciting world of data physicalization waiting to be explored, and the authors open the door for us and lead us through it with intelligent commentary. The book takes us to visit different artists, who explain their approaches and tools – from copper pipes to paper, from wood to electronics. It's a hugely inspiring tour. Reading this book will make you want to experiment with data in the realm of the physical."-- Lisa Charlotte Muth, data vis designer and writer at Datawrapper "This book has fresh inspirations from innovative artist-inventors who open up new possibilities for anyone who has data that tells a story. The screen is no longer the goal or the limit; freeing designers to explore more dimensions and shape deeper experiences to reach people with important messages about their health, communities, and climate. Data physicalizations break free into new dimensions where playful imaginations can use water, plastic, wood, or stone to fabricate data stories for public installations and private reflections. This book makes me want to turn on the laser cutter and restart the 3D printer to fabricate something startling, informative, and eye opening."-- Ben Shneiderman, Professor, Computer science, University of Maryland, USA"A collection of recent and diverse data-driven physical artifacts and sensorial experiences. Projects are beautifully illustrated and described in jargon-free language packed with practical information elucidating the design process, from the tools used to the context of their conception. Making with Data is an invaluable resource for educators and practitioners alike. It broadens our perspective of representing data by engaging all our senses."-- Isabel Meirelles, Professor, Faculty of Design, OCAD University, Toronto, Canada"“Designing with Data” is one of today’s key mantras. What next? Perhaps “Making with Data”, as argued by professors Huron, Nagel, Oehlberg and Willett. This timely book explores new ways data is penetrating our living environment and is crossing the boundary between the physical and the digital. Innovative fabrication methods lend materiality to data, as designers experiment with the use of laser cutters and 3D printers to transform maps and charts into tactile models and artworks. A compelling read for any data enthusiast!"-- Carlo Ratti, Director, MIT Senseable City Lab, USATable of Contents1. Handcraft - Introduction by Sheelagh Carpendale and Lora Oehlberg. 1.1 Snow Water Equivalent by Adrien Segal. 1.2 Life in Clay by Alice Thudt. 1.3 V-Pleat Data Origami by Sarah Hayes. 1.4 Anthropocene Footprints by Mieka West. 1.5 Endings by Loren Madsen. 2. Participation - Introduction by Georgia Panagiotidou and Andrew Vande Moere. 2.1 Cairn by Pauline Gourlet and Thierry Dassé. 2.2 SeeBoat by Laura Perovich. 2.3 Let’s Play with Data by Jose Duarte and EasyDataViz. 2.4 100% [City] by Rimini Protokoll (Helgard Haug, Stefan Kaegi, and Daniel Wetzel). 2.5 Data Strings by Daniel Pearson, Pau Garcia, and Alexandra de Requesens. 3. Digital Production - Introduction by Yvonne Jansen. 3.1 Chemo Singing Bowl by Stephen Barrass. 3.2 Wage Islands by Ekene Ijeoma. 3.3 Data That Feels Gravity by Volker Schweisfurth. 3.4 Orbacles by MINN_LAB Design Collective (Daniel F. Keefe, Ross Altheimer, Andrea J. Johnson, Mahdieh Mahmoudi, Patrick Moe, Maura Rockcastle, Marc Swackhamer, and Aaron Wittkamper). 3.5 Dataseeds by Nick Dulake and Ian Gwilt. 4 Actuation - Introduction by Pierre Dragicevic. 4.1 Tenison Road Charts by David Sweeney, Alex Taylor, and Siân Lindley. 4.2 LOOP by Kim Sauvé and Steven Houben. 4.3 AirFIELD by Nik Hafermaas, Dan Goods, and Jamie Barlow. 4.4 EMERGE by Jason Alexander, Faisal Taher, John Hardy, and John Vidler. 4.5 Zooids by Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, and Pierre Dragicevic. 5. Environment - Introduction by Dietmar Offenhuber. 5.1 Perpetual Plastic by Liina Klauss, Moritz Stefaner and Skye Morét. 5.2 Dataponics: Human-Vegetal Play by Robert Cercós. 5.3 Solar Totems by Charles Sowers. 5.4 Staubmarke (Dustmark) by Dietmar Offenhuber.

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Book SynopsisThis book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.Table of Contents1. Evolutionary Intelligence. 2. Heart Disease Diagnosis. 3. Diabetes Prediction and Classification. 4. Degenerative Diseases. 5. Tuberculosis. 6. Muscular Dystrophy. 7. Tumor Prediction and Classification.

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Book SynopsisThis book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learningTable of Contents1. Introduction 2. Overview of Python for Machine Learning 3. Data Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5. Supervised Learning: Regression 6. Supervised Learning: Classification 7. Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision Sciences Using Python

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Book SynopsisUrban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS).The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport.Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical ITable of ContentsPart I. Methods. 1. Introduction. 2. Data collection and analyses. 3. Geographic information systems and spatial analysis. 4. Optimisation. 5. Multi-agent simulation with machine learning. 6. Reliability and resilience. 7. Evaluation. Part II. Applications. 8. Autonomous Vehicles and Robots. 9. Access management and pricing. 10. Environmental sustainability. 11. Disruption of Networks. 12. Future directions.

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Book SynopsisKnowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.Table of Contents1. Introduction 2. Probabilistic Knowledge-based Systems 3. Consistency Measures for Probabilistic Knowledge Bases 4. Methods for Restoring Consistency in Probabilistic Knowledge Bases 5. Distance-Based Methods for Integrating Probabilistic Knowledge Bases 6. Value-based Method for Integrating Probabilistic Knowledge Bases 7. Experiments and Applications 8. Conclusions and Open Problems

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Book SynopsisRecommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

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Book SynopsisThe book focuses on a paradigm of blockchain technology that addresses cyber security. The challenges related to cyber security and the solutions based on Software Defined Networks are discussed. The book presents solutions to deal with cyber security attacks by considering real-time applications based on IoT, Wireless Sensor Networks, Cyber-Physical Systems, and Smart Grids. The book is useful for academicians and research scholars worldwide working in cyber security. It is also useful for industry experts working in cyber security.

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