Databases / Data management Books

Book SynopsisItzik Ben-Gan is a mentor with and co-founder of SolidQ. A Microsoft Data Platform MVP since 1999, Itzik has taught numerous training events around the world focused on T-SQL querying, query tuning, and programming. Itzik is the author of several books about T-SQL. He has written many articles for SQL Server Pro as well as articles and white papers for MSDN and The SolidQ Journal. Itzik's speaking engagements include Tech-Ed, SQL PASS, SQL Server Connections, presentations to various SQL Server user groups, and SolidQ events. Itzik is a subject-matter expert within SolidQ for its T-SQL related activities. He authored SolidQ's Advanced T-SQL and T-SQL Fundamentals courses and delivers them regularly worldwide. You can learn more about Itzik at http://tsql.solidq.com/.Table of ContentsCHAPTER 1: Background to T-SQL querying and programming CHAPTER 2: Single-table queries CHAPTER 3: Joins CHAPTER 4: Subqueries CHAPTER 5: Table expressions CHAPTER 6: Set operators CHAPTER 7: T-SQL for data analysis CHAPTER 8: Data modification CHAPTER 9: Temporal tables CHAPTER 10: Transactions and concurrency CHAPTER 11: SQL Graph CHAPTER 12: Programmable objects Appendix: Getting started

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Book SynopsisDatabase System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true. Table of ContentsChapter 1: IntroductionPart 1: Relational LanguagesChapter 2: Introduction to the Relational ModelChapter 3: Introduction to SQLChapter 4: Intermediate SQLChapter 5: Advanced SQLPart II: Database DesignChapter 6: Database Design Using the E-R ModelChapter 7: Relational Database DesignPart III: Application Design and DevelopmentChapter 8: Complex Data TypesChapter 9: Application DevelopmentPart IV: Big Data AnalyticsChapter 10: Big DataChapter 11: Data AnalyticsPart V: Storage Management and IndexingChapter 12: Physical Storage SystemsChapter 13: Data Storage StructuresChapter 14: IndexingPart VI: Query Processing and OptimizationChapter 15: Query ProcessingChapter 16: Query OptimizationPart VII: Transaction ManagementChapter 17: TransactionsChapter 18: Concurrency ControlChapter 19: Recovery SystemPart VIII: Parallel and Distributed DatabasesChapter 20: Database-System ArchitecturesChapter 21: Parallel and Distributed StorageChapter 22: Parallel and Distributed Query ProcessingChapter 23: Parallel and Distributed Transaction ProcessingPart IX: Advanced TopicsChapter 24: Advanced Indexing TechniquesChapter 25: Advanced Application DevelopmentChapter 26: Blockchain DatabasesPart X: Appendix AAppendix A: Detailed University SchemaPart XI: Online ChaptersChapter 27: Formal Relational Query LanguagesChapter 28: Advanced Relational Database DesignChapter 29: Object-Based DatabasesChapter 30: XMLChapter 31: Information RetrievalChapter 32: PostgreSQL

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Book SynopsisCourses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not

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Book SynopsisThis book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.“Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton"With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun“This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua BengioTable of ContentsPreface 3 1 The Deep Learning Revolution 19 1.1 The Impact of Deep Learning . . . . . . . . . . . . . . . . . . . . 20 1.1.1 Medical diagnosis . . . . . . . . . . . . . . . . . . . . . . 20 1.1.2 Protein structure . . . . . . . . . . . . . . . . . . . . . . . 21 1.1.3 Image synthesis . . . . . . . . . . . . . . . . . . . . . . . . 22 1.1.4 Large language models . . . . . . . . . . . . . . . . . . . . 23 1.2 A Tutorial Example . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.2.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.2.2 Linear models . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.2.3 Error function . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.2.4 Model complexity . . . . . . . . . . . . . . . . . . . . . . 27 1.2.5 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 30 1.2.6 Model selection . . . . . . . . . . . . . . . . . . . . . . . . 32 1.3 A Brief History of Machine Learning . . . . . . . . . . . . . . . . 34 1.3.1 Single-layer networks . . . . . . . . . . . . . . . . . . . . 35 1.3.2 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . 36 1.3.3 Deep networks . . . . . . . . . . . . . . . . . . . . . . . . 38 2 Probabilities 41 2.1 The Rules of Probability . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.1 A medical screening example . . . . . . . . . . . . . . . . 43 2.1.2 The sum and product rules . . . . . . . . . . . . . . . . . . 44 2.1.3 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 46 2.1.4 Medical screening revisited . . . . . . . . . . . . . . . . . 48 2.1.5 Prior and posterior probabilities . . . . . . . . . . . . . . . 49 2.1.6 Independent variables . . . . . . . . . . . . . . . . . . . . 49 2.2 Probability Densities . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.2.1 Example distributions . . . . . . . . . . . . . . . . . . . . 51 2.2.2 Expectations and covariances . . . . . . . . . . . . . . . . 52 2.3 The Gaussian Distribution . . . . . . . . . . . . . . . . . . . . . . 54 2.3.1 Mean and variance . . . . . . . . . . . . . . . . . . . . . . 55 2.3.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 55 2.3.3 Bias of maximum likelihood . . . . . . . . . . . . . . . . . 57 2.3.4 Linear regression . . . . . . . . . . . . . . . . . . . . . . . 58 2.4 Transformation of Densities . . . . . . . . . . . . . . . . . . . . . 60 2.4.1 Multivariate distributions . . . . . . . . . . . . . . . . . . . 62 2.5 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 64 2.5.1 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 2.5.2 Physics perspective . . . . . . . . . . . . . . . . . . . . . . 65 2.5.3 Differential entropy . . . . . . . . . . . . . . . . . . . . . . 67 2.5.4 Maximum entropy . . . . . . . . . . . . . . . . . . . . . . 68 2.5.5 Kullback–Leibler divergence . . . . . . . . . . . . . . . . . 69 2.5.6 Conditional entropy . . . . . . . . . . . . . . . . . . . . . 71 2.5.7 Mutual information . . . . . . . . . . . . . . . . . . . . . . 72 2.6 Bayesian Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 72 2.6.1 Model parameters . . . . . . . . . . . . . . . . . . . . . . . 73 2.6.2 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 74 2.6.3 Bayesian machine learning . . . . . . . . . . . . . . . . . . 75 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3 Standard Distributions 83 3.1 Discrete Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.1.1 Bernoulli distribution . . . . . . . . . . . . . . . . . . . . . 84 3.1.2 Binomial distribution . . . . . . . . . . . . . . . . . . . . . 85 3.1.3 Multinomial distribution . . . . . . . . . . . . . . . . . . . 86 3.2 The Multivariate Gaussian . . . . . . . . . . . . . . . . . . . . . . 88 3.2.1 Geometry of the Gaussian . . . . . . . . . . . . . . . . . . 89 3.2.2 Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3.2.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.2.4 Conditional distribution . . . . . . . . . . . . . . . . . . . 94 3.2.5 Marginal distribution . . . . . . . . . . . . . . . . . . . . . 97 3.2.6 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 99 3.2.7 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 102 3.2.8 Sequential estimation . . . . . . . . . . . . . . . . . . . . . 103 3.2.9 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . 104 3.3 Periodic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 107 3.3.1 Von Mises distribution . . . . . . . . . . . . . . . . . . . . 107 3.4 The Exponential Family . . . . . . . . . . . . . . . . . . . . . . . 112 3.4.1 Sufficient statistics . . . . . . . . . . . . . . . . . . . . . . 115 3.5 Nonparametric Methods . . . . . . . . . . . . . . . . . . . . . . . 116 3.5.1 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . 116 3.5.2 Kernel densities . . . . . . . . . . . . . . . . . . . . . . . . 118 3.5.3 Nearest-neighbours . . . . . . . . . . . . . . . . . . . . . . 121 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 4 Single-layer Networks: Regression 129 4.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 130 4.1.1 Basis functions . . . . . . . . . . . . . . . . . . . . . . . . 130 4.1.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 132 4.1.3 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 133 4.1.4 Geometry of least squares . . . . . . . . . . . . . . . . . . 135 4.1.5 Sequential learning . . . . . . . . . . . . . . . . . . . . . . 135 4.1.6 Regularized least squares . . . . . . . . . . . . . . . . . . . 136 4.1.7 Multiple outputs . . . . . . . . . . . . . . . . . . . . . . . 137 4.2 Decision theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 4.3 The Bias–Variance Trade-off . . . . . . . . . . . . . . . . . . . . . 141 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 5 Single-layer Networks: Classification 149 5.1 Discriminant Functions . . . . . . . . . . . . . . . . . . . . . . . . 150 5.1.1 Two classes . . . . . . . . . . . . . . . . . . . . . . . . . . 150 5.1.2 Multiple classes . . . . . . . . . . . . . . . . . . . . . . . . 152 5.1.3 1-of-K coding . . . . . . . . . . . . . . . . . . . . . . . . 153 5.1.4 Least squares for classification . . . . . . . . . . . . . . . . 154 5.2 Decision Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 5.2.1 Misclassification rate . . . . . . . . . . . . . . . . . . . . . 157 5.2.2 Expected loss . . . . . . . . . . . . . . . . . . . . . . . . . 158 5.2.3 The reject option . . . . . . . . . . . . . . . . . . . . . . . 160 5.2.4 Inference and decision . . . . . . . . . . . . . . . . . . . . 161 5.2.5 Classifier accuracy . . . . . . . . . . . . . . . . . . . . . . 165 5.2.6 ROC curve . . . . . . . . . . . . . . . . . . . . . . . . . . 166 5.3 Generative Classifiers . . . . . . . . . . . . . . . . . . . . . . . . 168 5.3.1 Continuous inputs . . . . . . . . . . . . . . . . . . . . . . 170 5.3.2 Maximum likelihood solution . . . . . . . . . . . . . . . . 171 5.3.3 Discrete features . . . . . . . . . . . . . . . . . . . . . . . 174 5.3.4 Exponential family . . . . . . . . . . . . . . . . . . . . . . 174 5.4 Discriminative Classifiers . . . . . . . . . . . . . . . . . . . . . . 175 5.4.1 Activation functions . . . . . . . . . . . . . . . . . . . . . 176 5.4.2 Fixed basis functions . . . . . . . . . . . . . . . . . . . . . 176 5.4.3 Logistic regression . . . . . . . . . . . . . . . . . . . . . . 177 5.4.4 Multi-class logistic regression . . . . . . . . . . . . . . . . 179 5.4.5 Probit regression . . . . . . . . . . . . . . . . . . . . . . . 181 5.4.6 Canonical link functions . . . . . . . . . . . . . . . . . . . 182 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 6 Deep Neural Networks 189 6.1 Limitations of Fixed Basis Functions . . . . . . . . . . . . . . . . 190 6.1.1 The curse of dimensionality . . . . . . . . . . . . . . . . . 190 6.1.2 High-dimensional spaces . . . . . . . . . . . . . . . . . . . 193 6.1.3 Data manifolds . . . . . . . . . . . . . . . . . . . . . . . . 194 6.1.4 Data-dependent basis functions . . . . . . . . . . . . . . . 196 6.2 Multilayer Networks . . . . . . . . . . . . . . . . . . . . . . . . . 198 6.2.1 Parameter matrices . . . . . . . . . . . . . . . . . . . . . . 199 6.2.2 Universal approximation . . . . . . . . . . . . . . . . . . . 199 6.2.3 Hidden unit activation functions . . . . . . . . . . . . . . . 200 6.2.4 Weight-space symmetries . . . . . . . . . . . . . . . . . . 203 6.3 Deep Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 6.3.1 Hierarchical representations . . . . . . . . . . . . . . . . . 205 6.3.2 Distributed representations . . . . . . . . . . . . . . . . . . 205 6.3.3 Representation learning . . . . . . . . . . . . . . . . . . . 206 6.3.4 Transfer learning . . . . . . . . . . . . . . . . . . . . . . . 207 6.3.5 Contrastive learning . . . . . . . . . . . . . . . . . . . . . 209 6.3.6 General network architectures . . . . . . . . . . . . . . . . 211 6.3.7 Tensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4 Error Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4.1 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4.2 Binary classification . . . . . . . . . . . . . . . . . . . . . 214 6.4.3 multiclass classification . . . . . . . . . . . . . . . . . . . 215 6.5 Mixture Density Networks . . . . . . . . . . . . . . . . . . . . . . 216 6.5.1 Robot kinematics example . . . . . . . . . . . . . . . . . . 216 6.5.2 Conditional mixture distributions . . . . . . . . . . . . . . 217 6.5.3 Gradient optimization . . . . . . . . . . . . . . . . . . . . 219 6.5.4 Predictive distribution . . . . . . . . . . . . . . . . . . . . 220 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 7 Gradient Descent 227 7.1 Error Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 7.1.1 Local quadratic approximation . . . . . . . . . . . . . . . . 229 7.2 Gradient Descent Optimization . . . . . . . . . . . . . . . . . . . 231 7.2.1 Use of gradient information . . . . . . . . . . . . . . . . . 232 7.2.2 Batch gradient descent . . . . . . . . . . . . . . . . . . . . 232 7.2.3 Stochastic gradient descent . . . . . . . . . . . . . . . . . . 232 7.2.4 Mini-batches . . . . . . . . . . . . . . . . . . . . . . . . . 234 7.2.5 Parameter initialization . . . . . . . . . . . . . . . . . . . . 234 7.3 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 7.3.1 Momentum . . . . . . . . . . . . . . . . . . . . . . . . . . 238 7.3.2 Learning rate schedule . . . . . . . . . . . . . . . . . . . . 240 7.3.3 RMSProp and Adam . . . . . . . . . . . . . . . . . . . . . 241 7.4 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 7.4.1 Data normalization . . . . . . . . . . . . . . . . . . . . . . 244 7.4.2 Batch normalization . . . . . . . . . . . . . . . . . . . . . 245 7.4.3 Layer normalization . . . . . . . . . . . . . . . . . . . . . 247 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 8 Backpropagation 251 8.1 Evaluation of Gradients . . . . . . . . . . . . . . . . . . . . . . . 252 8.1.1 Single-layer networks . . . . . . . . . . . . . . . . . . . . 252 8.1.2 General feed-forward networks . . . . . . . . . . . . . . . 253 8.1.3 A simple example . . . . . . . . . . . . . . . . . . . . . . 256 8.1.4 Numerical differentiation . . . . . . . . . . . . . . . . . . . 257 8.1.5 The Jacobian matrix . . . . . . . . . . . . . . . . . . . . . 258 8.1.6 The Hessian matrix . . . . . . . . . . . . . . . . . . . . . . 260 8.2 Automatic Differentiation . . . . . . . . . . . . . . . . . . . . . . 262 8.2.1 Forward-mode automatic differentiation . . . . . . . . . . . 264 8.2.2 Reverse-mode automatic differentiation . . . . . . . . . . . 267 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 9 Regularization 271 9.1 Inductive Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 9.1.1 Inverse problems . . . . . . . . . . . . . . . . . . . . . . . 272 9.1.2 No free lunch theorem . . . . . . . . . . . . . . . . . . . . 273 9.1.3 Symmetry and invariance . . . . . . . . . . . . . . . . . . . 274 9.1.4 Equivariance . . . . . . . . . . . . . . . . . . . . . . . . . 277 9.2 Weight Decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 9.2.1 Consistent regularizers . . . . . . . . . . . . . . . . . . . . 280 9.2.2 Generalized weight decay . . . . . . . . . . . . . . . . . . 282 9.3 Learning Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 9.3.1 Early stopping . . . . . . . . . . . . . . . . . . . . . . . . 284 9.3.2 Double descent . . . . . . . . . . . . . . . . . . . . . . . . 286 9.4 Parameter Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . 288 9.4.1 Soft weight sharing . . . . . . . . . . . . . . . . . . . . . . 289 9.5 Residual Connections . . . . . . . . . . . . . . . . . . . . . . . . 292 9.6 Model Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 9.6.1 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 10 Convolutional Networks 305 10.1 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 10.1.1 Image data . . . . . . . . . . . . . . . . . . . . . . . . . . 307 10.2 Convolutional Filters . . . . . . . . . . . . . . . . . . . . . . . . . 308 10.2.1 Feature detectors . . . . . . . . . . . . . . . . . . . . . . . 308 10.2.2 Translation equivariance . . . . . . . . . . . . . . . . . . . 309 10.2.3 Padding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 10.2.4 Strided convolutions . . . . . . . . . . . . . . . . . . . . . 312 10.2.5 Multi-dimensional convolutions . . . . . . . . . . . . . . . 313 10.2.6 Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 10.2.7 Multilayer convolutions . . . . . . . . . . . . . . . . . . . 316 10.2.8 Example network architectures . . . . . . . . . . . . . . . . 317 10.3 Visualizing Trained CNNs . . . . . . . . . . . . . . . . . . . . . . 320 10.3.1 Visual cortex . . . . . . . . . . . . . . . . . . . . . . . . . 320 10.3.2 Visualizing trained filters . . . . . . . . . . . . . . . . . . . 321 10.3.3 Saliency maps . . . . . . . . . . . . . . . . . . . . . . . . 323 10.3.4 Adversarial attacks . . . . . . . . . . . . . . . . . . . . . . 324 10.3.5 Synthetic images . . . . . . . . . . . . . . . . . . . . . . . 326 10.4 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 10.4.1 Bounding boxes . . . . . . . . . . . . . . . . . . . . . . . 327 10.4.2 Intersection-over-union . . . . . . . . . . . . . . . . . . . . 328 10.4.3 Sliding windows . . . . . . . . . . . . . . . . . . . . . . . 329 10.4.4 Detection across scales . . . . . . . . . . . . . . . . . . . . 331 10.4.5 Non-max suppression . . . . . . . . . . . . . . . . . . . . . 332 10.4.6 Fast region CNNs . . . . . . . . . . . . . . . . . . . . . . . 332 10.5 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 333 10.5.1 Convolutional segmentation . . . . . . . . . . . . . . . . . 333 10.5.2 Up-sampling . . . . . . . . . . . . . . . . . . . . . . . . . 334 10.5.3 Fully convolutional networks . . . . . . . . . . . . . . . . . 336 10.5.4 The U-net architecture . . . . . . . . . . . . . . . . . . . . 337 10.6 Style Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 11 Structured Distributions 343 11.1 Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 11.1.1 Directed graphs . . . . . . . . . . . . . . . . . . . . . . . . 344 11.1.2 Factorization . . . . . . . . . . . . . . . . . . . . . . . . . 345 11.1.3 Discrete variables . . . . . . . . . . . . . . . . . . . . . . . 347 11.1.4 Gaussian variables . . . . . . . . . . . . . . . . . . . . . . 350 11.1.5 Binary classifier . . . . . . . . . . . . . . . . . . . . . . . 352 11.1.6 Parameters and observations . . . . . . . . . . . . . . . . . 352 11.1.7 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 354 11.2 Conditional Independence . . . . . . . . . . . . . . . . . . . . . . 355 11.2.1 Three example graphs . . . . . . . . . . . . . . . . . . . . 356 11.2.2 Explaining away . . . . . . . . . . . . . . . . . . . . . . . 359 11.2.3 D-separation . . . . . . . . . . . . . . . . . . . . . . . . . 361 11.2.4 Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . 362 11.2.5 Generative models . . . . . . . . . . . . . . . . . . . . . . 364 11.2.6 Markov blanket . . . . . . . . . . . . . . . . . . . . . . . . 365 11.2.7 Graphs as filters . . . . . . . . . . . . . . . . . . . . . . . . 366 11.3 Sequence Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 11.3.1 Hidden variables . . . . . . . . . . . . . . . . . . . . . . . 370 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 12 Transformers 375 12.1 Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 12.1.1 Transformer processing . . . . . . . . . . . . . . . . . . . . 378 12.1.2 Attention coefficients . . . . . . . . . . . . . . . . . . . . . 379 12.1.3 Self-attention . . . . . . . . . . . . . . . . . . . . . . . . . 380 12.1.4 Network parameters . . . . . . . . . . . . . . . . . . . . . 381 12.1.5 Scaled self-attention . . . . . . . . . . . . . . . . . . . . . 384 12.1.6 Multi-head attention . . . . . . . . . . . . . . . . . . . . . 384 12.1.7 Transformer layers . . . . . . . . . . . . . . . . . . . . . . 386 12.1.8 Computational complexity . . . . . . . . . . . . . . . . . . 388 12.1.9 Positional encoding . . . . . . . . . . . . . . . . . . . . . . 389 12.2 Natural Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 12.2.1 Word embedding . . . . . . . . . . . . . . . . . . . . . . . 393 12.2.2 Tokenization . . . . . . . . . . . . . . . . . . . . . . . . . 395 12.2.3 Bag of words . . . . . . . . . . . . . . . . . . . . . . . . . 396 12.2.4 Autoregressive models . . . . . . . . . . . . . . . . . . . . 397 12.2.5 Recurrent neural networks . . . . . . . . . . . . . . . . . . 398 12.2.6 Backpropagation through time . . . . . . . . . . . . . . . . 399 12.3 Transformer Language Models . . . . . . . . . . . . . . . . . . . . 400 12.3.1 Decoder transformers . . . . . . . . . . . . . . . . . . . . . 401 12.3.2 Sampling strategies . . . . . . . . . . . . . . . . . . . . . . 404 12.3.3 Encoder transformers . . . . . . . . . . . . . . . . . . . . . 406 12.3.4 Sequence-to-sequence transformers . . . . . . . . . . . . . 408 12.3.5 Large language models . . . . . . . . . . . . . . . . . . . . 408 12.4 Multimodal Transformers . . . . . . . . . . . . . . . . . . . . . . 412 12.4.1 Vision transformers . . . . . . . . . . . . . . . . . . . . . . 413 12.4.2 Generative image transformers . . . . . . . . . . . . . . . . 414 12.4.3 Audio data . . . . . . . . . . . . . . . . . . . . . . . . . . 417 12.4.4 Text-to-speech . . . . . . . . . . . . . . . . . . . . . . . . 418 12.4.5 Vision and language transformers . . . . . . . . . . . . . . 420 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 13 Graph Neural Networks 425 13.1 Machine Learning on Graphs . . . . . . . . . . . . . . . . . . . . 427 13.1.1 Graph properties . . . . . . . . . . . . . . . . . . . . . . . 428 13.1.2 Adjacency matrix . . . . . . . . . . . . . . . . . . . . . . . 428 13.1.3 Permutation equivariance . . . . . . . . . . . . . . . . . . . 429 13.2 Neural Message-Passing . . . . . . . . . . . . . . . . . . . . . . . 430 13.2.1 Convolutional filters . . . . . . . . . . . . . . . . . . . . . 431 13.2.2 Graph convolutional networks . . . . . . . . . . . . . . . . 432 13.2.3 Aggregation operators . . . . . . . . . . . . . . . . . . . . 434 13.2.4 Update operators . . . . . . . . . . . . . . . . . . . . . . . 436 13.2.5 Node classification . . . . . . . . . . . . . . . . . . . . . . 437 13.2.6 Edge classification . . . . . . . . . . . . . . . . . . . . . . 438 13.2.7 Graph classification . . . . . . . . . . . . . . . . . . . . . . 438 13.3 General Graph Networks . . . . . . . . . . . . . . . . . . . . . . . 438 13.3.1 Graph attention networks . . . . . . . . . . . . . . . . . . . 439 13.3.2 Edge embeddings . . . . . . . . . . . . . . . . . . . . . . . 439 13.3.3 Graph embeddings . . . . . . . . . . . . . . . . . . . . . . 440 13.3.4 Over-smoothing . . . . . . . . . . . . . . . . . . . . . . . 440 13.3.5 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 441 13.3.6 Geometric deep learning . . . . . . . . . . . . . . . . . . . 442 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 14 Sampling 447 14.1 Basic Sampling Algorithms . . . . . . . . . . . . . . . . . . . . . 448 14.1.1 Expectations . . . . . . . . . . . . . . . . . . . . . . . . . 448 14.1.2 Standard distributions . . . . . . . . . . . . . . . . . . . . 449 14.1.3 Rejection sampling . . . . . . . . . . . . . . . . . . . . . . 451 14.1.4 Adaptive rejection sampling . . . . . . . . . . . . . . . . . 453 14.1.5 Importance sampling . . . . . . . . . . . . . . . . . . . . . 455 14.1.6 Sampling-importance-resampling . . . . . . . . . . . . . . 457 14.2 Markov Chain Monte Carlo . . . . . . . . . . . . . . . . . . . . . 458 14.2.1 The Metropolis algorithm . . . . . . . . . . . . . . . . . . 459 14.2.2 Markov chains . . . . . . . . . . . . . . . . . . . . . . . . 460 14.2.3 The Metropolis–Hastings algorithm . . . . . . . . . . . . . 463 14.2.4 Gibbs sampling . . . . . . . . . . . . . . . . . . . . . . . . 464 14.2.5 Ancestral sampling . . . . . . . . . . . . . . . . . . . . . . 468 14.3 Langevin Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 469 14.3.1 Energy-based models . . . . . . . . . . . . . . . . . . . . . 470 14.3.2 Maximizing the likelihood . . . . . . . . . . . . . . . . . . 471 14.3.3 Langevin dynamics . . . . . . . . . . . . . . . . . . . . . . 472 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 15 Discrete Latent Variables 477 15.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 478 15.1.1 Image segmentation . . . . . . . . . . . . . . . . . . . . . 482 15.2 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . . . . . 484 15.2.1 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 486 15.2.2 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 488 15.3 Expectation–Maximization Algorithm . . . . . . . . . . . . . . . . 492 15.3.1 Gaussian mixtures . . . . . . . . . . . . . . . . . . . . . . 496 15.3.2 Relation to K-means . . . . . . . . . . . . . . . . . . . . . 498 15.3.3 Mixtures of Bernoulli distributions . . . . . . . . . . . . . . 499 15.4 Evidence Lower Bound . . . . . . . . . . . . . . . . . . . . . . . 503 15.4.1 EM revisited . . . . . . . . . . . . . . . . . . . . . . . . . 504 15.4.2 Independent and identically distributed data . . . . . . . . . 506 15.4.3 Parameter priors . . . . . . . . . . . . . . . . . . . . . . . 507 15.4.4 Generalized EM . . . . . . . . . . . . . . . . . . . . . . . 507 15.4.5 Sequential EM . . . . . . . . . . . . . . . . . . . . . . . . 508 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 16 Continuous Latent Variables 513 16.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . 515 16.1.1 Maximum variance formulation . . . . . . . . . . . . . . . 515 16.1.2 Minimum-error formulation . . . . . . . . . . . . . . . . . 517 16.1.3 Data compression . . . . . . . . . . . . . . . . . . . . . . . 519 16.1.4 Data whitening . . . . . . . . . . . . . . . . . . . . . . . . 520 16.1.5 High-dimensional data . . . . . . . . . . . . . . . . . . . . 522 16.2 Probabilistic Latent Variables . . . . . . . . . . . . . . . . . . . . 524 16.2.1 Generative model . . . . . . . . . . . . . . . . . . . . . . . 524 16.2.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 525 16.2.3 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 527 16.2.4 Factor analysis . . . . . . . . . . . . . . . . . . . . . . . . 531 16.2.5 Independent component analysis . . . . . . . . . . . . . . . 532 16.2.6 Kalman filters . . . . . . . . . . . . . . . . . . . . . . . . . 533 16.3 Evidence Lower Bound . . . . . . . . . . . . . . . . . . . . . . . 534 16.3.1 Expectation maximization . . . . . . . . . . . . . . . . . . 536 16.3.2 EM for PCA . . . . . . . . . . . . . . . . . . . . . . . . . 537 16.3.3 EM for factor analysis . . . . . . . . . . . . . . . . . . . . 538 16.4 Nonlinear Latent Variable Models . . . . . . . . . . . . . . . . . . 540 16.4.1 Nonlinear manifolds . . . . . . . . . . . . . . . . . . . . . 540 16.4.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 542 16.4.3 Discrete data . . . . . . . . . . . . . . . . . . . . . . . . . 544 16.4.4 Four approaches to generative modelling . . . . . . . . . . 545 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 17 Generative Adversarial Networks 551 17.1 Adversarial Training . . . . . . . . . . . . . . . . . . . . . . . . . 552 17.1.1 Loss function . . . . . . . . . . . . . . . . . . . . . . . . . 553 17.1.2 GAN training in practice . . . . . . . . . . . . . . . . . . . 554 17.2 Image GANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 17.2.1 CycleGAN . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562 18 Normalizing Flows 565 18.1 Coupling Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 18.2 Autoregressive Flows . . . . . . . . . . . . . . . . . . . . . . . . . 570 18.3 Continuous Flows . . . . . . . . . . . . . . . . . . . . . . . . . . 572 18.3.1 Neural differential equations . . . . . . . . . . . . . . . . . 572 18.3.2 Neural ODE backpropagation . . . . . . . . . . . . . . . . 573 18.3.3 Neural ODE flows . . . . . . . . . . . . . . . . . . . . . . 575 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 19 Autoencoders 581 19.1 Deterministic Autoencoders . . . . . . . . . . . . . . . . . . . . . 582 19.1.1 Linear autoencoders . . . . . . . . . . . . . . . . . . . . . 582 19.1.2 Deep autoencoders . . . . . . . . . . . . . . . . . . . . . . 583 19.1.3 Sparse autoencoders . . . . . . . . . . . . . . . . . . . . . 584 19.1.4 Denoising autoencoders . . . . . . . . . . . . . . . . . . . 585 19.1.5 Masked autoencoders . . . . . . . . . . . . . . . . . . . . . 585 19.2 Variational Autoencoders . . . . . . . . . . . . . . . . . . . . . . . 587 19.2.1 Amortized inference . . . . . . . . . . . . . . . . . . . . . 590 19.2.2 The reparameterization trick . . . . . . . . . . . . . . . . . 592 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596 20 Diffusion Models 599 20.1 Forward Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 20.1.1 Diffusion kernel . . . . . . . . . . . . . . . . . . . . . . . 601 20.1.2 Conditional distribution . . . . . . . . . . . . . . . . . . . 602 20.2 Reverse Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 20.2.1 Training the decoder . . . . . . . . . . . . . . . . . . . . . 605 20.2.2 Evidence lower bound . . . . . . . . . . . . . . . . . . . . 606 20.2.3 Rewriting the ELBO . . . . . . . . . . . . . . . . . . . . . 607 20.2.4 Predicting the noise . . . . . . . . . . . . . . . . . . . . . . 609 20.2.5 Generating new samples . . . . . . . . . . . . . . . . . . . 610 20.3 Score Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 20.3.1 Score loss function . . . . . . . . . . . . . . . . . . . . . . 613 20.3.2 Modified score loss . . . . . . . . . . . . . . . . . . . . . . 614 20.3.3 Noise variance . . . . . . . . . . . . . . . . . . . . . . . . 615 20.3.4 Stochastic differential equations . . . . . . . . . . . . . . . 616 20.4 Guided Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 20.4.1 Classifier guidance . . . . . . . . . . . . . . . . . . . . . . 618 20.4.2 Classifier-free guidance . . . . . . . . . . . . . . . . . . . 618 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 Appendix A Linear Algebra 627 A.1 Matrix Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 A.2 Traces and Determinants . . . . . . . . . . . . . . . . . . . . . . . 628 A.3 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Book SynopsisThis book takes the reader through the process of learning and creating data visualisation, following a unique journey with questions every step of the way, ultimately discussing how and when to bend and break the rules to come up with creative, unique, and sometimes unconventional ideas. Each easy-to-follow chapter poses one key question and provides a selection of discussion points and relevant data visualisation examples throughout.Structured in three parts: Section I poses questions around some fundamental data visualisation principles, while Section II introduces more advanced questions, challenging perceived best practices and suggesting when rules are open to interpretation or there to be broken. The questions in Section III introduce further themes leading on to specific ideas and visualisation projects in more detail.Questions in Dataviz: A Design-Driven Process for Data Visualisation will appeal to any reader with an interest in creaTrade Review"It’s a common experience for newcomers in visualization to be a bit disoriented. Here are some questions we’ve all asked ourselves at some point: Am I doing things correctly? Should I use this type of chart or that other type? Is this color palette appropriate? Will my intended audience understand the point I’m trying to make with this graphic? Will they be able to use the graphic’s interface? Am I breaking any rule of visualization? And so many others. The difference between Neil Richards and the rest of us is that Neil has written an entire book about his posing those questions to himself, and about the journey towards trying to answer them. Spoiler alert: the journey is often circuitous, and sometimes lacks a clear destination. But who cares? The journey, and not where it leads, is what can make us wiser as professionals; the process of reasoning to disentangle complex design choices has a value of its own.Moreover, and perhaps more importantly, walking that path along someone as friendly and personable as Neil makes the experience exciting and joyful."– Alberto Cairo and Tamara Munzner, Series Editors, AK Peters Visualization Series"Questions in Dataviz is an amazing resource for data visualisation folks looking for different and more creative design ideas - instead of following the norms of business data visualisation it asks the questions that challenge conventional practices to inspire new ideas to develop your own style and data visualisation philosophy. Neil introduces us to the concepts, inspirations and designers that inspired him, and encourages you to ask questions to find your own design driven journey into more creative design-driven output."– Giorgia Lupi, Pentagram"Beyond technical skills, statistical knowledge, and creative talent, one of the most vital attributes in data visualisation is to be curious. Before a chart comes data. Before the data comes a question. Questions fuel one’s understanding about anything and in this super new book, Neil Richards eloquently demonstrates his amazing flair for being curious. He answers the questions he had – and that anyone else should have – about the journey towards successfully mastering data visualisation. He delightfully unpacks the whys, the why nots, and the hows of this complex subject, in a wonderfully engaging and perfectly nuanced way."– Andy Kirk, Visualising Data Ltd."Neil writes about the 'why' behind his own chart design decisions in an engaging way that will give any new practitioner a glimpse inside the brain of a data visualization designer, with examples that showcase how an individual designer's style evolves and changes over time. For the experienced practitioner, Neil's book offers a tour through the many questions about our motivations and design decisions in data visualization that have emerged over the past decade or more. In some ways, the ideas feel like a delightful highlights reel of debates and discussions born out on Twitter and in various slack channels, summarized neatly and without judgement around the ways we may come to different answers to those questions."– Amanda Makulec, Executive Director, Data Visualization Society"Neil is a luminary in the field and his work clearly pushes the boundaries of data visualization. This book will help people push past the "standard" chart types and consider different, alternative visualizations that they may not have considered before."– Jonathan Schwabish, Urban Institute and PolicyViz“When do we break the rules? What are the exceptions? What is the decision making process that goes into creating dataviz and how do you bend the universal principles based on specific circumstances? This book explores these questions in an open-minded way.”– Valentina D'Efilippo, Award-winning data designerTable of ContentsPreface. Author. Introduction. SECTION I First Questions. Chapter 1.1 Should the data drive the visualisation? Chapter 1.2 What’s in a colour? Chapter 1.3 What does data visualisation have in common with psychology? Chapter 1.4 Do data visualisations have to tell a story? Chapter 1.5 Is it OK to steal? Chapter 1.6 Is white space always your friend? Section II Challenging. Questions Chapter 2.1 Why do we visualise data? Chapter 2.2 Why do we visualise using triangles? Chapter 2.3 Does it matter if shapes overlap? Chapter 2.4 What is data humanism? Chapter 2.5 What is design-driven data? Chapter 2.6 Do we take data visualisation too seriously? Chapter 2.7 Why create unnecessary data visualisations? Chapter 2.8 When are several visualisations better than one? Chapter 2.9 What can I do when data is impossible to find? Section III Idea Questions. Chapter 3.1 What is the third wave of data visualisation? Chapter 3.2 What alternative ways are there for visualizing timelines? Chapter 3.3 Why do I use flowers to visualise data? Chapter 3.4 What are Data Portraits? Chapter 3.5 How can I take inspiration from album covers? Chapter 3.6 How many ways can you tile the United States? Chapter 3.7 Is it possible to tile the world? Chapter 3.8 Can you create visualisations using only numbers? Chapter 3.9 How do you visualise music? Chapter 3.10 What are Truchet tiles? Chapter 3.11 How do you create 31 visualisations in a month? Index.

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Book SynopsisApply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics. Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult! Key features include: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects! About the technology Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.

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Book SynopsisDr. William Stallings hasauthored 19 titles, and counting revised editions, over 40 books on computersecurity, computer networking, and computer architecture. His writings haveappeared in numerous publications, including the Proceedings of the IEEE,ACM Computing Reviews and Cryptologia. He has received 13 times theaward for the best Computer Science textbook of the year from the Text andAcademic Authors Association.  In over 30 years in thefield, he has been a technical contributor, technical manager, and an executivewith several high-technology firms. He has designeTable of Contents Computer and Network Security Concepts Introduction to Number Theory Classical Encryption Techniques Block Ciphers and the Data Encryption Standard Finite Fields Advanced Encryption Standard Block Cipher Operation Random Bit Generation and Stream Ciphers Public-Key Cryptography and RSA Other Public-Key Cryptosystems Cryptographic Hash Functions Message Authentication Codes Digital Signatures Lightweight Cryptography and Post-Quantum Cryptography Key Management and Distribution User Authentication Protocols Transport-Level Security Wireless Network Security Electronic Mail Security IP Security Network Endpoint Security Cloud Security Internet of Things (IoT) Security Appendix A. Basic Concepts from Linear Algebra Appendix B. Measures of Security and Secrecy Appendix C. Data Encryption Standard (DES) Appendix D. Simplified AES Appendix E. Mathematical Basis of the Birthday Attack

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Book SynopsisScott W. Ambler is a software process improvement (SPI) consultant living just north of Toronto. He is founder and practice leader of the Agile Modeling (AM) (www.agilemodeling.com), Agile Data (AD) (www.agiledata.org), Enterprise Unified Process (EUP) (www.enterpriseunifiedprocess.com), and Agile Unified Process (AUP) (www.ambysoft.com/unifiedprocess) methodologies. Scott is the (co-)author of several books, including Agile Modeling (John Wiley & Sons, 2002), Agile Database Techniques (John Wiley & Sons, 2003), The Object Primer, Third Edition (Cambridge University Press, 2004), The Enterprise Unified Process (Prentice Hall, 2005), and The Elements of UML 2.0 Style (Cambridge University Press, 2005). Scott is a contributing editor with Software Development magazine (www.sdmagazine.com) and has spoken and keynoted at a wide variety of international conferences, including Software Development, UML World,Table of ContentsAbout the Authors xv Forewords xvii Preface xxi Acknowledgments xxvii Chapter 1: Evolutionary Database Development 1 Chapter 2: Database Refactoring 13 Chapter 3: The Process of Database Refactoring 29 Chapter 4: Deploying into Production 49 Chapter 5: Database Refactoring Strategies 59 Chapter 6: Structural Refactorings 69 Chapter 7: Data Quality Refactorings 151 Chapter 8: Referential Integrity Refactorings 203 Chapter 9: Architectural Refactorings 231 Chapter 10: Method Refactorings 277 Chapter 11: Transformations 295 Appendix: The UML Data Modeling Notation 315 Glossary 321 References and Recommended Reading 327 Index 331

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Book SynopsisTeach database theory with the bestselling text on the subject Database Systems: A Practical Approach to Design, Implementation, and Management introduces the theory behind databases in a concise yet comprehensive manner. Designed for undergraduate courses, the text is accessible for non-technical readers. This title comes with a Companion Website.Table of ContentsPart 1 Background Chapter 1 Introduction to Databases Database Environment Database Architectures and the Web Part 2 The Relational Model and Languages Chapter 4 The Rational Model Chapter 5 Relational Algebra and Relational Calculus Chapter 6 SQL: Data Manipulation Chapter 7 SQL: Data Definition Chapter 8 Advanced SQL Chapter 9 Object-Relational DBMSs Part 3 Database Analysis and Design Chapter 10 Database System Development Lifecycle Chapter 11 Database Analysis and the DreamHome Case Study Chapter 12 Entity-Relationship Modeling Chapter 13 Enhanced Entity-Relationship Modeling Chapter 14 Normalization Chapter 15 Advanced Normalization Part 4 Methodology Chapter 16 Methodology – Conceptual Database Design Chapter 17 Methodology – Logical Database Design for the Relational Model Chapter 18 Methodology – Physical Database Design for Relational Databases Chapter 19 Methodology – Monitoring and Tuning the Operational System Part 5 Selected Database Issues Chapter 20 Security and Administration Chapter 21 Professional, Legal, and Ethical Issues in Data Management Chapter 22 Transaction Management Chapter 23 Query Processing Part 6 Distributed DBMSs and Replication Chapter 24 Distributed DBMSs – Concepts and Design Chapter 25 Distributed DBMSs – Advanced Concepts Chapter 26 Replication and Mobile Databases Part 7 Object DBMSs Chapter 27 Object-Oriented DBMSs – Concepts and Design Chapter 28 Object-Oriented DBMSs – Standard Systems Part 8 The Web and DBMSs Chapter 29 Web Technology and DBMSs Chapter 30 Semistructured Data and XML Part 9 Business Intelligence Chapter 31 Data Warehousing Concepts Chapter 32 Data Warehousing Design Chapter 33 OLAP Chapter 34 Data Mining Appendices References Further Reading Index

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Book SynopsisThe key to successful database development is accurate and appropriate modelling of the real-world system that will be placed on the computer. This guide describes two popular modelling methods, the entity-relationship model and the semantic object model.Table of ContentsIntroduction 1 Part I: Basic Concepts 7 Chapter 1: Database Processing 9 Chapter 2: Database Development 21 Part II: Data Modeling: What Should the Database Represent? 39 Chapter 3: The Users’ Model 41 Chapter 4: The Entity-Relationship Model 49 Chapter 5: The Semantic Object Model 67 Chapter 6: Determining What You Are Going to Do 89 Part III: Database Design 103 Chapter 7: The Relational Model 105 Chapter 8: Using an Entity-Relationship Model to Design a Database 129 Chapter 9: Using a Semantic Object Model to Design a Database 141 Part IV: Implementing a Database 159 Chapter 10: Using DBMS Tools to Implement a Database 161 Chapter 11: Addressing Bigger Problems with SQL Server 2000 199 Chapter 12: Using SQL to Implement a Database 217 Part V: Implementing a Database Application 229 Chapter 13: Using DBMS Tools to Implement a Database Application 231 Chapter 14: SQL and Database Applications 251 Part VI: Using Internet Technology with Database 257 Chapter 15: Database on Networks 259 Chapter 16: Database Security and Reliability 271 Part VII: The Part of Tens 281 Chapter 17: Ten Rules to Remember When Creating a Database 283 Chapter 18: Ten Rules to Remember When Creating a Database Application 289 Glossary 293 Index 305

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Book SynopsisWith this updated edition, application architects, developers, and production engineers new to the Kafka streaming platform will learn how to handle data in motion. Additional chapters cover Kafka's AdminClient API, transactions, new security features, and tooling changes.

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Book SynopsisDoes the subject of data analysis make you dizzy? This book features introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages. It helps you to identify valid, useful, and understandable patterns in data.Table of ContentsIntroduction 1 Part I: Introducing Big Data Statistics 7 Chapter 1: What Is Big Data and What Do You Do With It? 9 Chapter 2: Characteristics of Big Data: The Three Vs 19 Chapter 3: Using Big Data: The Hot Applications 27 Chapter 4: Understanding Probabilities 41 Chapter 5: Basic Statistical Ideas 57 Part II: Preparing and Cleaning Data 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis 83 Chapter 7: Figuring the Format: Important Computer File Formats 99 Chapter 8: Checking Assumptions: Testing for Normality 107 Chapter 9: Dealing with Missing or Incomplete Data 119 Chapter 10: Sending Out a Posse: Searching for Outliers 129 Part III: Exploratory Data Analysis (EDA) 141 Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143 Chapter 12: A Plot to Get Graphical: Graphical Techniques 155 Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173 Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191 Chapter 15: Regression Analysis 215 Chapter 16: When You’ve Got the Time: Time Series Analysis 243 Part IV: Big Data Applications 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271 Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297 Chapter 19: Seeking Free Sources of Financial Data 319 Part V: The Part of Tens 331 Chapter 20: Ten (or So) Best Practices in Data Preparation 333 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339 Index 349

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Book SynopsisDescription Phoenix is a modern web framework built for the Elixir programming language. Elegant, fault-tolerant, and performant, Phoenix is as easy to use as Rails and as rock-solid as Elixir’s Erlang-based foundation. Phoenix in Action builds on your existing web dev skills, teaching you the unique benefits of Phoenix along with just enough Elixir to get the job done. Phoenix in Action is an example-based tutorial that teaches you how to use the Phoenix framework to build production-quality web apps. Following a running example of an online auction site, you’ll design and build everything from the core components that drive the app to the real-time user interactions where Phoenix really shines. You’ll handle business logic, database interactions, and app designs that take advantage of functional programming as you discover a better way to develop web applications. Key features · Use channels for real-time communication · Learn database interactions with Ecto · Hands-on examples · Step-by-step instructions · Jargon-free Audience Written for web developers familiar with a framework like Rails or ASP.NET. No experience of Elixir or Phoenix required. About the technology Phoenix is a web framework for the Elixir language. Phoenix applications are blazingly fast, and as a developer you’ll appreciate the attention to detail in the framework design that makes you superproductive almost immediately. In particular, Phoenix channels provide an easy way to set up and manage real-time communication.

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Book Synopsis Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook/professional book/video authors. Dr. Harvey Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experienTrade Review“The chapters are clearly written with detailed explanations of the example code. The modular structure, wide range of contemporary data science topics, and code in companion Jupyter notebooks make this a fantastic resource for readers of a variety of backgrounds. Fabulous Big Data chapter—it covers all of the relevant programs and platforms. Great Watson chapter! The chapter provides a great overview of the Watson applications. Also, your translation examples are great because they provide an ‘instant reward’—it’s very satisfying to implement a task and receive results so quickly. Machine Learning is a huge topic, and the chapter serves as a great introduction. I loved the California housing data example—very relevant for business analytics. The chapter was visually stunning.” —Alison Sanchez, Assistant Professor in Economics, University of San Diego “A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical. The authors do an excellent job of combining programming and data science topics. The material is presented in digestible sections accompanied by engaging interactive examples. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python—the use of card image graphics is sure to engage the reader.” —Garrett Dancik, Eastern Connecticut State University “Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice. I can see readers feeling really excited about playing with the animations.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “An engaging, highly accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs—sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other—with an emphasis on using JSON over pickle—good to know there’s a better, safer way!” —Jamie Whitacre, Data Science Consultant “For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic.” —Ramon Mata-Toledo, Professor, James Madison University “I like the new combination of topics from computer science, data science, and stats. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go.” —Lance Bryant, Shippensburg University “You’ll develop applications using industry standard libraries and cloud computing services.” —Daniel Chen, Data Scientist, Lander Analytics “Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science.” —Shyamal Mitra, Senior Lecturer, University of Texas “IBM Watson is an exciting chapter. The code examples put together a lot of Watson services in a really nifty example.” —Daniel Chen, Data Scientist, Lander Analytics “Fun, engaging real-world examples will encourage readers to conduct meaningful data analyses. Provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what ‘the best’ means. Great overview of all the big data technologies with relevant examples.” —Jamie Whitacre, Data Science Consultant “A great introduction to deep learning.” —Alison Sanchez, University of San Diego “The best designed Intro to Data Science/Python book I have seen.” —Roland DePratti, Central Connecticut State University “I like the new combination of topics from computer science, data science, and stats.” —Lance Bryant, Shippensburg University “The book’s applied approach should engage readers. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” —Garrett Dancik, Assoc. Prof. of Computer Science/Bioinformatics, Eastern Connecticut State University “Helps readers leverage the large number of existing libraries to accomplish tasks with minimal code. Concepts are accompanied by rich Python examples that readers can adapt to implement their own solutions to data science problems. I like that cloud services are used.” —David Koop, Assistant Professor, U-Mass Dartmouth “I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling example are just great.” —Daniel Chen, Data Scientist, Lander Analytics “A lucid exposition of the fundamentals of Python and Data Science. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. ‘List vs. Array Performance: Introducing %timeit’ is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it.” —Shyamal Mitra, Senior Lecturer, University of Texas “I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool.” —David Koop, Assistant Professor, U-Mass Dartmouth “I love the book! The examples are definitely a high point.” —Dr. Irene Bruno, George Mason University “I was very excited to see this book. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.” —Dr. Harvey Siy, University of Nebraska at Omaha “I’ve learned a lot in this review process, discovering the exciting field of AI. I’ve liked the Deep Learning chapter, which has left me amazed with the things that have already been achieved in this field.” —José Antonio González Seco, Consultant “An impressive hands-on approach to programming meant for exploration and experimentation.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example.” —David Koop, Assistant Professor, U-Mass Dartmouth “Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the case-study chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones.” —Daniel Chen, Data Scientist, Lander Analytics “I really liked the live IPython input-output. The thing that I like most about this product is that it is a Deitel & Deitel book (I’m a big fan) that covers Python.” —Dr. Mark Pauley, University of Nebraska at Omaha Table of ContentsPreface xviiBefore You Begin xxxiiiChapter 1: Introduction to Computers and Python 11.1 Introduction 21.2 A Quick Review of Object Technology Basics 31.3 Python 51.4 It’s the Libraries! 71.5 Test-Drives: Using IPython and Jupyter Notebooks 91.6 The Cloud and the Internet of Things 161.7 How Big Is Big Data? 171.8 Case Study—A Big-Data Mobile Application 241.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 261.10 Wrap-Up 29Chapter 2: Introduction to Python Programming 312.1 Introduction 322.2 Variables and Assignment Statements 322.3 Arithmetic 332.4 Function print and an Intro to Single- and Double-Quoted Strings 362.5 Triple-Quoted Strings 382.6 Getting Input from the User 392.7 Decision Making: The if Statement and Comparison Operators 412.8 Objects and Dynamic Typing 452.9 Intro to Data Science: Basic Descriptive Statistics 462.10 Wrap-Up 48Chapter 3: Control Statements 493.1 Introduction 503.2 Control Statements 503.3 if Statement 513.4 if...else and if...elif...else Statements 523.5 while Statement 553.6 for Statement 553.7 Augmented Assignments 573.8 Sequence-Controlled Iteration; Formatted Strings 583.9 Sentinel-Controlled Iteration 593.10 Built-In Function range: A Deeper Look 603.11 Using Type Decimal for Monetary Amounts 613.12 break and continue Statements 643.13 Boolean Operators and, or and not 653.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 673.15 Wrap-Up 69Chapter 4: Functions 714.1 Introduction 724.2 Defining Functions 724.3 Functions with Multiple Parameters 754.4 Random-Number Generation 764.5 Case Study: A Game of Chance 784.6 Python Standard Library 814.7 math Module Functions 824.8 Using IPython Tab Completion for Discovery 834.9 Default Parameter Values 854.10 Keyword Arguments 854.11 Arbitrary Argument Lists 864.12 Methods: Functions That Belong to Objects 874.13 Scope Rules 874.14 import: A Deeper Look 894.15 Passing Arguments to Functions: A Deeper Look 904.16 Recursion 934.17 Functional-Style Programming 954.18 Intro to Data Science: Measures of Dispersion 974.19 Wrap-Up 98Chapter 5: Sequences: Lists and Tuples 1015.1 Introduction 1025.2 Lists 1025.3 Tuples 1065.4 Unpacking Sequences 1085.5 Sequence Slicing 1105.6 del Statement 1125.7 Passing Lists to Functions 1135.8 Sorting Lists 1155.9 Searching Sequences 1165.10 Other List Methods 1175.11 Simulating Stacks with Lists 1195.12 List Comprehensions 1205.13 Generator Expressions 1215.14 Filter, Map and Reduce 1225.15 Other Sequence Processing Functions 1245.16 Two-Dimensional Lists 1265.17 Intro to Data Science: Simulation and Static Visualizations 1285.18 Wrap-Up 135Chapter 6: Dictionaries and Sets 1376.1 Introduction 1386.2 Dictionaries 1386.3 Sets 1476.4 Intro to Data Science: Dynamic Visualizations 1526.5 Wrap-Up 158Chapter 7: Array-Oriented Programming with NumPy 1597.1 Introduction 1607.2 Creating arrays from Existing Data 1607.3 array Attributes 1617.4 Filling arrays with Specific Values 1637.5 Creating arrays from Ranges 1647.6 List vs. array Performance: Introducing %timeit 1657.7 array Operators 1677.8 NumPy Calculation Methods 1697.9 Universal Functions 1707.10 Indexing and Slicing 1717.11 Views: Shallow Copies 1737.12 Deep Copies 1747.13 Reshaping and Transposing 1757.14 Intro to Data Science: pandas Series and DataFrames 1777.15 Wrap-Up 189Chapter 8: Strings: A Deeper Look 1918.1 Introduction 1928.2 Formatting Strings 1938.3 Concatenating and Repeating Strings 1968.4 Stripping Whitespace from Strings 1978.5 Changing Character Case 1978.6 Comparison Operators for Strings 1988.7 Searching for Substrings 1988.8 Replacing Substrings 1998.9 Splitting and Joining Strings 2008.10 Characters and Character-Testing Methods 2028.11 Raw Strings 2038.12 Introduction to Regular Expressions 2038.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 2108.14 Wrap-Up 214Chapter 9: Files and Exceptions 2179.1 Introduction 2189.2 Files 2199.3 Text-File Processing 2199.4 Updating Text Files 2229.5 Serialization with JSON 2239.6 Focus on Security: pickle Serialization and Deserialization 2269.7 Additional Notes Regarding Files 2269.8 Handling Exceptions 2279.9 finally Clause 2319.10 Explicitly Raising an Exception 2339.11 (Optional) Stack Unwinding and Tracebacks 2339.12 Intro to Data Science: Working with CSV Files 2359.13 Wrap-Up 241Chapter 10: Object-Oriented Programming 24310.1 Introduction 24410.2 Custom Class Account 24610.3 Controlling Access to Attributes 24910.4 Properties for Data Access 25010.5 Simulating “Private” Attributes 25610.6 Case Study: Card Shuffling and Dealing Simulation 25810.7 Inheritance: Base Classes and Subclasses 26610.8 Building an Inheritance Hierarchy; Introducing Polymorphism 26710.9 Duck Typing and Polymorphism 27510.10 Operator Overloading 27610.11 Exception Class Hierarchy and Custom Exceptions 27910.12 Named Tuples 28010.13 A Brief Intro to Python 3.7’s New Data Classes 28110.14 Unit Testing with Docstrings and doctest 28710.15 Namespaces and Scopes 29010.16 Intro to Data Science: Time Series and Simple Linear Regression 29310.17 Wrap-Up 301Chapter 11: Natural Language Processing (NLP) 30311.1 Introduction 30411.2 TextBlob 30511.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 31911.4 Readability Assessment with Textatistic 32411.5 Named Entity Recognition with spaCy 32611.6 Similarity Detection with spaCy 32711.7 Other NLP Libraries and Tools 32811.8 Machine Learning and Deep Learning Natural Language Applications 32811.9 Natural Language Datasets 32911.10 Wrap-Up 330Chapter 12: Data Mining Twitter 33112.1 Introduction 33212.2 Overview of the Twitter APIs 33412.3 Creating a Twitter Account 33512.4 Getting Twitter Credentials—Creating an App 33512.5 What’s in a Tweet? 33712.6 Tweepy 34012.7 Authenticating with Twitter Via Tweepy 34112.8 Getting Information About a Twitter Account 34212.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 34412.10 Searching Recent Tweets 34712.11 Spotting Trends: Twitter Trends API 34912.12 Cleaning/Preprocessing Tweets for Analysis 35312.13 Twitter Streaming API 35412.14 Tweet Sentiment Analysis 35912.15 Geocoding and Mapping 36212.16 Ways to Store Tweets 37012.17 Twitter and Time Series 37012.18 Wrap-Up 371Chapter 13: IBM Watson and Cognitive Computing 37313.1 Introduction: IBM Watson and Cognitive Computing 37413.2 IBM Cloud Account and Cloud Console 37513.3 Watson Services 37613.4 Additional Services and Tools 37913.5 Watson Developer Cloud Python SDK 38113.6 Case Study: Traveler’s Companion Translation App 38113.7 Watson Resources 39413.8 Wrap-Up 395Chapter 14: Machine Learning: Classification, Regression and Clustering 39714.1 Introduction to Machine Learning 39814.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 40314.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 41314.4 Case Study: Time Series and Simple Linear Regression 42014.5 Case Study: Multiple Linear Regression with the California Housing Dataset 42514.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 43814.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 44214.8 Wrap-Up 455Chapter 15: Deep Learning 45715.1 Introduction 45815.2 Keras Built-In Datasets 46115.3 Custom Anaconda Environments 46215.4 Neural Networks 46315.5 Tensors 46515.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 46715.7 Visualizing Neural Network Training with TensorBoard 48615.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 48915.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 48915.10 Tuning Deep Learning Models 49715.11 Convnet Models Pretrained on ImageNet 49815.12 Wrap-Up 499Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 50116.1 Introduction 50216.2 Relational Databases and Structured Query Language (SQL) 50616.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 51716.4 Case Study: A MongoDB JSON Document Database 52016.5 Hadoop 53016.6 Spark 54116.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 55116.8 Internet of Things and Dashboards 56016.9 Wrap-Up 571Index 573

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Book SynopsisA provocative look at how data is reinventing the market: where big firms will no longer be dominant.Trade ReviewIdeas on how best to organise a data economy are far and few between. This book offers plenty of food for thought * Ludwig Siegele, Technology Editor, The Economist *This landmark book . . . should challenge and inspire every corporate strategist and public policy maker * Philip Evans, Senior Advisor, The Boston Consulting Group *Anyone interested in the future of business should read this fascinating book . . . Reinventing Capitalism makes a compelling case that it will change the nature of the market itself. With brilliant insights, it explains how the shift from simple price signalling to data-rich preference matching will determine the winners and losers of the 21st century economy, and thoughtfully outlines steps to curb the excesses of this new environment * Kevin Werbach, The Wharton School, University of Pennsylvania *Praise for Big Data:An optimistic and practical look at the big data revolution - just the thing to get your head around the big changes already underway and the bigger changes to come * Cory Doctorow, Boing Boing *Teems with great insights on the new ways of harnessing information, and offers a convincing vision of the future. It is essential reading for anyone who uses - or is affected by - big data * Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity Analytics *An excellent primer * Financial Times *Fascinating * Observer *

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Book SynopsisThis book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.Table of Contents1. An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits 2. Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach 3. Object Detection and Tracking in Video Using Deep Learning Techniques: A Review 4. Fuzzy MCDM: Application in Disease Risk and Prediction 5. Deep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural Networks 6. A Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based Approaches 7. Detection of Retinopathy of Prematurity Using Convolution Neural Network 8. Impact of Technology on Human Resource Information System and Achieving Business Intelligence in Organizations 9. Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm 10. Role of Machine Learning in Social Area Networks 11. Breast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naïve Bayes Algorithm 12. Deep Networks and Deep Learning Algorithms 13. Machine Learning for Big Data Analytics, Interactive and Reinforcement 14. Fish Farm Monitoring System Using IoT and Machine Learning

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Book SynopsisRandolph West (they/them) lives in Calgary, Alberta, Canada, with a husband and two dogs. After being a consultant for millennia, Randolph now writes full-time at Microsoft Docs, still yelling at the screen. Occasional voice actor. Occasional blogger at bornsql.ca. Not to be trusted around chocolate. Yes, this is a short bio. William Assaf (he/him) is a senior content developer for Microsoft, writing Learn content for SQL Server, Azure SQL Database, Azure Synapse Analytics, and more. A long-time Baton Rougean, William and his adventure buddy Christine moved to Seattle during the pandemic. They love their new home but are still New Orleans Saints fans. Before joining Microsoft, William was a Data Platform MVP, SQL Saturday and SQL community organizer, and a long-time DBA and data consultant. As a consultant for 13 years, he worked with clients across the U.S. on SQL Server and Azure SQL platform optimization, management, data integration, disa

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Book SynopsisLearn the intricate workings of DAX and the mechanics that are necessary to solve advanced Power BI challenges. This book is all about DAX (Data Analysis Expressions), the formula language used in Power BI-Microsoft''s leading self-service business intelligence application-and covers other products such as PowerPivot and SQL Server Analysis Services Tabular. You will learn how to leverage the advanced applications of DAX to solve complex tasks.Often a task seems complex due to a lack of understanding, or a misunderstanding of core principles, and how certain components interact with each other. The authors of this book use solutions and examples to teach you how to solve complex problems. They explain the intricate workings of important concepts such as Filter Context and Context Transition. You will learn how Power BI, through combining DAX building blocks (such as measures, table filtering, and data lineage), can yield extraordinary analytical power. Throughout Table of ContentsPart I: The FoundationChapter 1: DAX MechanicChapter 2: Data ModelingChapter 3: DAX LineagePart II: Core ConceptsChapter 4: This Weird Context ThingChapter 5: Filtering in DAXChapter 6: IteratorsChapter 7: Filters Using Measures Part III: DAX to Solve Advanced Everyday ProblemsChapter 8: Using DAX to Solve Advanced Reporting RequirementsChapter 9: Time IntelligenceChapter 10: Finding What's Not ThereChapter 11: Row Level SecurityPart IV: Debugging and OptimizationChapter 12: DAX StudioChapter 13: Query PlansChapter 14: Scale your Models

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Book SynopsisPublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.The definitive guide to dimensional design for your data warehouseLearn the best practices of dimensional design. Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses throuTable of ContentsPart I: Fundamentals; Chapter 1: Analytic Databases and Dimensional Design; Chapter 2: Data Warehouse Architectures; Chapter 3: Stars and Cubes; Part II: Multiple Stars; Chapter 4: A Fact Table for Each Process; Chapter 5: Conformed Dimensions; Part III: Dimension Design; Chapter 6: More on Dimension Tables; Chapter 7: Hierarchies and Snowflakes; Chapter 8: More Slow Change Techniques; Chapter 9: Multi-Value Dimensions and Bridges; Chapter 10: Recursive Hierarchies and Bridges;Part IV: Fact Table Design; Chapter 11: Transactions, Snapshots and Accumulating Snapshots; Chapter 12: Factless Fact Tables; Chapter 13: Type-Specific Stars; Part V: Performance; Chapter 14: Derived Schemas; Chapter 15: Aggregates; Part VI: Tools and Documentation; Chapter 16: Design and Business Intelligence; Chapter 17: Design and ETL; Chapter 18: How to Design and Document a Dimensional Model; Index

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Book SynopsisPublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Take Your PL/SQL Programming Skills to the Next LevelBuild robust database-centric PL/SQL applications quickly and effectively. Oracle Database 12c PL/SQL Advanced Programming Techniques shows you how to write and deploy Java libraries inside Oracle Database 12c, use the utl_file and DBMS_SCHEDULER packages, and create external tables and external procedures. Application security, performance tuning, and Oracle Database In-Memory are also covered in this Oracle Press guide. Configure, deploy, and troubleshoot Java libraries for Oracle object types Table of ContentsSection 1 - Java in the DatabaseChapter 1. Functions & ProceduresChapter 2. Object TypesSection 2 - File I/OChapter 3. UTL_FILE PackageChapter 4. Java I/OChapter 5. External TablesChapter 6. Data Pump & StreamsSection 3 - Application SecurityChapter 7. Security by DesignChapter 8. Developing ApplicationsSection 4 – Applied TechnologiesChapter 9. Intersession CommunicationChapter 10. DBMS_SCHEDULE R PackageChapter 11. External ProceduresChapter 12. PL/SQL in TimesTenAppendixesAppendix A. Java PrimerAppendix B. Mastery Check

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Book SynopsisYour Oracle career starts here! Ideal for those new to Oracle technology, this officially authorized guide explains in easy-to-follow detail how to administer an Oracle database using this state-of-the-art tool. Inside, you'll learn to eliminate, simplify, and automate administrative tasks and use Oracle Enterprise Manager (EM) as a management framework for your entire Oracle environment.

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Book SynopsisWritten for those with Acrobat experience, and seeking to take advantage of the feature enhancements of either the Standard or Professional version of Acrobat 6.0. This work teaches the techniques for creating professional PDFs for print, the web, or CD. The author produces the Adobe-supported PDF Conference.Table of ContentsPart I: PDF Standards for Everyone 1 What's New in the Acrobat and PDF Universe 2 Why Quality Matters 3 Creating the Best PDF for the Job 4 Making Onscreen PDFs 5 Making a PDF Everyone Can Read Part II: In Business with Acrobat 6 Putting Acrobat to Work 7 Working Together: Acrobat Collaboration 8 PDF from Corel WordPerfect 9 Working with Acrobat Security and Digital Signatures 10 The Wide World of Acrobat 11 Moving Beyond One-Document-at-a-Time Creation 12 PDF Forms 13 Introduction to Adobe Acrobat JavaScript 14 A Short Course for System Administrators Part III: Acrobat for Creative Professionals 15 Adobe Products 16 Corel Applications 17 PDF from QuarkXPress 18 Preflighting and Color Printing

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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 Synopsis Laura Graesser is a research software engineer working in robotics at Google. She holds a master's degree in computer science from New York University, where she specialized in machine learning. Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science. Trade Review“This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.” –Volodymyr Mnih, lead developer of DQN “An excellent book to quickly develop expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A limpid exposition which uses familiar notation; all the most recent techniques explained with concise, readable code, and not a page wasted in irrelevant detours: it is the perfect way to develop a solid foundation on the topic.” –Vincent Vanhoucke, principal scientist, Google “As someone who spends their days trying to make deep reinforcement learning methods more useful for the general public, I can say that Laura and Keng’s book is a welcome addition to the literature. It provides both a readable introduction to the fundamental concepts in reinforcement learning as well as intuitive explanations and code for many of the major algorithms in the field. I imagine this will become an invaluable resource for individuals interested in learning about deep reinforcement learning for years to come.” –Arthur Juliani, senior machine learning engineer, Unity Technologies “Until now, the only way to get to grips with deep reinforcement learning was to slowly accumulate knowledge from dozens of different sources. Finally, we have a book bringing everything together in one place.” –Matthew Rahtz, ML researcher, ETH ZürichTable of ContentsForeword xixPreface xxiAcknowledgments xxvAbout the Authors xxvii Chapter 1: Introduction to Reinforcement Learning 1 1.1 Reinforcement Learning 1 1.2 Reinforcement Learning as MDP 6 1.3 Learnable Functions in Reinforcement Learning 9 1.4 Deep Reinforcement Learning Algorithms 11 1.5 Deep Learning for Reinforcement Learning 17 1.6 Reinforcement Learning and Supervised Learning 19 1.7 Summary 21 Part I: Policy-Based and Value-Based Algorithms 23 Chapter 2: REINFORCE 25 2.1 Policy 26 2.2 The Objective Function 26 2.3 The Policy Gradient 27 2.4 Monte Carlo Sampling 30 2.5 REINFORCE Algorithm 31 2.6 Implementing REINFORCE 33 2.7 Training a REINFORCE Agent 44 2.8 Experimental Results 47 2.9 Summary 51 2.10 Further Reading 51 2.11 History 51 Chapter 3: SARSA 53 3.1 The Q- and V-Functions 54 3.2 Temporal Difference Learning 56 3.3 Action Selection in SARSA 65 3.4 SARSA Algorithm 67 3.5 Implementing SARSA 69 3.6 Training a SARSA Agent 74 3.7 Experimental Results 76 3.8 Summary 78 3.9 Further Reading 79 3.10 History 79 Chapter 4: Deep Q-Networks (DQN) 81 4.1 Learning the Q-Function in DQN 82 4.2 Action Selection in DQN 83 4.3 Experience Replay 88 4.4 DQN Algorithm 89 4.5 Implementing DQN 91 4.6 Training a DQN Agent 96 4.7 Experimental Results 99 4.8 Summary 101 4.9 Further Reading 102 4.10 History 102 Chapter 5: Improving DQN 103 5.1 Target Networks 104 5.2 Double DQN 106 5.3 Prioritized Experience Replay (PER) 109 5.4 Modified DQN Implementation 112 5.5 Training a DQN Agent to Play Atari Games 123 5.6 Experimental Results 128 5.7 Summary 132 5.8 Further Reading 132 Part II: Combined Methods 133Chapter 6: Advantage Actor-Critic (A2C) 135 6.1 The Actor 136 6.2 The Critic 136 6.3 A2C Algorithm 141 6.4 Implementing A2C 143 6.5 Network Architecture 148 6.6 Training an A2C Agent 150 6.7 Experimental Results 157 6.8 Summary 161 6.9 Further Reading 162 6.10 History 162 Chapter 7: Proximal Policy Optimization (PPO) 165 7.1 Surrogate Objective 165 7.2 Proximal Policy Optimization (PPO) 174 7.3 PPO Algorithm 177 7.4 Implementing PPO 179 7.5 Training a PPO Agent 182 7.6 Experimental Results 188 7.7 Summary 192 7.8 Further Reading 192 Chapter 8: Parallelization Methods 195 8.1 Synchronous Parallelization 196 8.2 Asynchronous Parallelization 197 8.3 Training an A3C Agent 200 8.4 Summary 203 8.5 Further Reading 204 Chapter 9: Algorithm Summary 205Part III: Practical Details 207Chapter 10: Getting Deep RL to Work 209 10.1 Software Engineering Practices 209 10.2 Debugging Tips 218 10.3 Atari Tricks 228 10.4 Deep RL Almanac 231 10.5 Summary 238 Chapter 11: SLM Lab 239 11.1 Algorithms Implemented in SLM Lab 239 11.2 Spec File 241 11.3 Running SLM Lab 246 11.4 Analyzing Experiment Results 247 11.5 Summary 249 Chapter 12: Network Architectures 251 12.1 Types of Neural Networks 251 12.2 Guidelines for Choosing a Network Family 256 12.3 The Net API 262 12.4 Summary 271 12.5 Further Reading 271 Chapter 13: Hardware 273 13.1 Computer 273 13.2 Data Types 278 13.3 Optimizing Data Types in RL 280 13.4 Choosing Hardware 285 13.5 Summary 285 Part IV: Environment Design 287Chapter 14: States 289 14.1 Examples of States 289 14.2 State Completeness 296 14.3 State Complexity 297 14.4 State Information Loss 301 14.5 Preprocessing 306 14.6 Summary 313 Chapter 15: Actions 315 15.1 Examples of Actions 315 15.2 Action Completeness 318 15.3 Action Complexity 319 15.4 Summary 323 15.5 Further Reading: Action Design in Everyday Things 324 Chapter 16: Rewards 327 16.1 The Role of Rewards 327 16.2 Reward Design Guidelines 328 16.3 Summary 332 Chapter 17: Transition Function 333 17.1 Feasibility Checks 333 17.2 Reality Check 335 17.3 Summary 337 Epilogue 338Appendix A: Deep Reinforcement Learning Timeline 343Appendix B: Example Environments 345 B.1 Discrete Environments 346 B.2 Continuous Environments 350 References 353Index 363

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Book SynopsisDr. William Stallings has made a unique contribution to understanding the broad sweep of technical developments in computer security, computer networking, and computer architecture. He has authored 18 textbooks and, counting revised editions, a total of 70 books on various aspects of these subjects. His writings have appeared in numerous ACM and IEEE publications, including the Proceedings of the IEEE and ACM Computing Reviews. He has 13 times received the award for the best computer science textbook of the year from the Text and Academic Authors Association. With more than 30 years in the field, he has been a technical contributor, a technical manager, and an executive with several high-technology firms. He has designed and implemented both TCP/IP-based and OSI-based protocol suites on a variety of computers and operating systems, ranging from microcomputers to mainframes. Currently he is an independent consultant whose clients have included computer and Table of Contents Part I: Planning for Privacy 1. Information Privacy Concepts 2. Security Governance and Management 3. Risk Assessment Part II: Privacy Threats 4. Information Storage and Processing 5. Information Collection and Dissemination 6. Intrusion and Interference Part III: Information Privacy Technology 7. Basic Privacy Controls 8. Privacy Enhancing Technology 9. Data Loss Prevention 10. Online Privacy 11. Detection of Conflicts In Security Policies 12. Privacy Evaluation Part IV: Information Privacy Regulations 13. GDPR 14. U.S. Privacy Laws and Regulations

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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 SynopsisDaniel A. Seara is an experienced software developer. He has more than 20 years as a technical instructor, developer, and development consultant. Daniel has worked as a software consultant in a wide range of companies in Argentina, Spain, and Peru. He has been asked by Peruvian Microsoft Consulting Services to help several companies in their migration path to .NET development. Daniel was Argentina's Microsoft regional director for four years and was the first nominated global regional director, a position he held for two years. He also was the manager of the Desarrollador Cinco Estrellas I (Five Star Developer) program, one of the most successful training projects in Latin America. Daniel held Visual Basic MVP status for more than 10 years, as well as SharePoint Server MVP status from 2008 until 2014. Additionally, Daniel is the founder and dean of Universidad. NET, the most-visited Spanish-Table of Contents1. Understanding Azure Data Solutions 2. Implementing Azure Data Storage Solutions 3. Managing and Developing Data Processing for Azure Data Solutions 4. Monitoring and Optimizing Azure Data Solutions

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Book SynopsisRyan Stephens is an entrepreneur who has built his career and multiple IT companies around SQL, data, and relational databases. He has shared his knowledge and experience with organizations, students, and IT professionals all over the world. Two of the companies he has co-founded, Perpetual Technologies, Inc. (PTI), and Indy Data Partners, have provided expert database and IT services to large-scale government and commercial clients for more than 25 years. Ryan has authored several books for Pearson, including Sams Teach Yourself SQL in 24 Hours, 6th Edition; some of his books have been translated and published internationally. Additionally, Ryan has worked for large organizations and has consulted within the areas of SQL, database design, database management, and project management. He designed and taught a database management program for Indiana University-Purdue University in Indianapolis and currently teaches online SQL and database classes for Pearson Education. Table of ContentsHour 1: Understanding the Relational Database and SQL Thriving in a Data-Driven World Understanding the Relational Database The Relational Database Continues to Lead the Way Examples and Exercises Summary Q&A Workshop Hour 2: Exploring the Components of the SQL Language SQL Definition and History SQL: The Standard Language SQL Sessions Types of SQL Commands Summary Q&A Workshop Hour 3: Getting to Know Your Data The BIRD Database: Examples and Exercises in This Book How to Talk About the Data Entity Relationship Diagrams Examples and Exercises Summary Q&A Workshop Hour 4: Setting Up Your Database Locating the Files You Need Getting Set Up for Hands-on Exercises List of Data by Table Summary Q&A Workshop Hour 5: Understanding the Basics of Relational (SQL) Database Design Understanding What Database Design Has to Do with SQL The Database Design Process Choosing a Database Design Methodology Using a Simple Process to Think Through the Design of the BIRDS Database Logical Model vs. Physical Design Database Life Cycle Summary Q&A Workshop Hour 6: Defining Entities and Relationships Creating a Data Model Based on Your Data Defining Relationships Employing Referential Integrity Creating an Entity Relationship Summary Q&A Workshop Hour 7: Normalizing Your Database Defining Normalization Exploring the Most Common Normal Forms of the Normalization Process Denormalizing a Database Applying Normalization to Your Database Summary Q&A Workshop Hour 8: Defining Data Structures Defining Data Understanding Basic Data Types Using Data Types in the BIRDS Database Summary Q&A Workshop Hour 9: Creating and Managing Database Objects Database Objects and Schemas Tables: The Primary Storage for Data Integrity Constraints Summary Q&A Workshop Hour 10: Manipulating Data Getting an Overview of Data Manipulation Populating Tables with New Data Updating Existing Data Deleting Data from Tables Summary Q&A Workshop Hour 11: Managing Database Transactions Defining Transactions Controlling Transactions Dealing with Poor Transactional Control Summary Q&A Workshop Hour 12: Introduction to Database Queries Using the SELECT Statement Case Sensitivity Fundamentals of Query Writing Summary Q&A Workshop Hour 13: Using Operators to Categorize Data Defining an Operator in SQL Using Comparison Operators Using Logical Operators Using Conjunctive Operators Using Negative Operators Using Arithmetic Operators Summary Q&A Workshop Hour 14: Joining Tables in Queries Selecting Data from Multiple Tables Understanding Joins Join Considerations Summary Q&A Workshop Hour 15: Restructuring the Appearance of Data ANSI Character Functions Common Character Functions Miscellaneous Character Functions Mathematical Functions Conversion Functions Combined Character Functions Summary Q&A Workshop Hour 16: Understanding Dates and Times Understanding How a Date Is Stored Using Date Functions Converting Dates Summary Q&A Workshop Hour 17: Summarizing Data Results from a Query Using Aggregate Functions Grouping Data Using the GROUP BY Clause Understanding the Difference Between GROUP BY and ORDER BY Using CUBE and ROLLUP Expressions Using the HAVING Clause Summary Q&A Workshop Hour 18: Using Subqueries to Define Unknown Data Defining Subqueries Embedded Subqueries Using Correlated Subqueries Summary Q&A Workshop Hour 19: Combining Multiple Queries into One Differentiating Single Queries and Compound Queries Using Compound Query Operators Using ORDER BY with a Compound Query Using GROUP BY with a Compound Query Retrieving Accurate Data Summary Q&A Workshop Hour 20: Creating and Using Views and Synonyms Defining Views Creating Views Updating Data Through a View Dropping a View Understanding the Performance Impact of Nested Views Defining Synonyms Summary Q&A Workshop Hour 21: Managing Database Users and Security Managing Users in the Database Understanding the Management Process Maximizing Tools Utilized by Database Users Understanding Database Security Assigning Privileges Controlling User Access Controlling Privileges Through Roles Summary Q&A Workshop Hour 22: Using Indexes to Improve Performance Defining an Index Understanding How Indexes Work Using the CREATE INDEX Command Identifying Types of Indexes Knowing When to Consider Using an Index Knowing When to Avoid Indexes Altering an Index Dropping an Index Summary Q&A Workshop Hour 23: Improving Database Performance Defining SQL Statement Tuning Comparing Database Tuning and SQL Statement Tuning Formatting Your SQL Statement Running Full Table Scans Identifying Other Performance Considerations Using Cost-Based Optimization Summary Q&A Workshop Hour 24: Working with the System Catalog Defining the System Catalog Creating the System Catalog Determining What Is Contained in the System Catalog Identifying System Catalog Tables by Implementation Querying the System Catalog Updating System Catalog Objects Summary Q&A Workshop Hour 25: Bonus Workshop for the Road The BIRDS Database Predators of Birds Photographers of Birds Creating the New Tables Workshop: Describing Your Tables Workshop: Basic Queries Workshop: Adding Tables Workshop: Manipulating Data Workshop: Joining Tables Workshop: Comparison Operators Workshop: Logical Operators Workshop: Conjunctive Operators Workshop: Arithmetic Operators Workshop: Character Functions Workshop: Aggregating Data Workshop: GROUP BY and HAVING Workshop: Composite Queries Workshop: Creating Tables from Existing Tables Workshop: Inserting Data into a Table from Another Table Workshop: Creating Views Workshop: Embedding Subqueries Workshop: Creating Views from Subqueries Workshop: Generating SQL Code from a SQL Statement Summary Workshop Appendix A: Common SQL Commands SQL Statements SQL Query Clauses Appendix B: Popular Vendor RDBMS Implementations Installing the Oracle Database Software Used for Examples and Hands-On Exercises Appendix C: Answers to Quizzes and Exercises Hour 1, "Understanding the Relational Database and SQL" Hour 2, "Exploring the Components of the SQL Language" Hour 3, "Getting to Know Your Data" Hour 4, "Setting Up Your Database" Hour 5, "Understanding the Basics of Relational (SQL) Database Design" Hour 6, "Defining Entities and Relationships" Hour 7, "Normalizing Your Database" Hour 8, "Defining Data Structures" Hour 9, "Creating and Managing Database Objects" Hour 10, "Manipulating Data" Hour 11, "Managing Database Transactions" Hour 12, "Introduction to Database Queries" Hour 13, "Using Operators to Categorize Data" Hour 14, "Joining Tables in Queries" Hour 15, "Restructuring the Appearance of Data" Hour 16, "Understanding Dates and Times" Hour 17, "Summarizing Data Results from a Query" Hour 18, "Using Subqueries to Define Unknown Data" Hour 19, "Combining Multiple Queries into One" Hour 20, "Creating and Using Views and Synonyms" Hour 21, "Managing Database Users and Security" Hour 22, "Using Indexes to Improve Performance" Hour 23, "Improving Database Performance" Hour 24, "Working with the System Catalog" Hour 25, "Bonus Workshop for the Road" 9780137543120 TOC 11/8/2021

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Book SynopsisBen Forta is Adobe's Senior Director of Education Initiatives and has more than three decades of experience in the computer industryin product development, support, training, and product marketing. He is the author of the best-selling Sams Teach Yourself SQL in 10 Minutes (as well as spinoff titles like this one and versions on SQL Server T-SQL, Oracle PL/SQL, and MariaDB), Learning Regular Expressions, and Captain Code, which teaches Python to younger coders (and those young at heart), Java, Windows, and more. He has extensive experience in database design and development, has implemented databases for several highly successful commercial software programs and websites, and is a frequent lecturer and columnist on application development and Internet technologies. Ben lives in Oak Park, Michigan, with his wife, Dr. Marcy Forta, and their children. He welcomes your email at ben@forta.com and invites you to visit his website at http://forta.Table of ContentsChapter 1 Understanding SQL 1 Database Basics 1 What Is a Database? 2 Tables 2 Columns and Datatypes 3 Rows 4 Primary Keys 4 What Is SQL? 6 Try It Yourself 6 Summary 7 Chapter 2 Introducing MySQL 9 What Is MySQL? 9 Client/Server Software 9 MySQL Versions 10 MySQL Tools 11 mysql Command-Line Utility 11 MySQL Workbench 12 Other Tools 13 Summary 13 Chapter 3 Working with MySQL 15 Using the Command-Line Tool 15 Selecting a Database 16 Learning About Databases and Tables 17 Using MySQL Workbench 20 Getting Started 20 Using MySQL Workbench 21 Selecting a Database 22 Learning About Databases and Tables 22 Executing SQL Statements 23 Next Steps 23 Summary 24 Chapter 4 Retrieving Data 25 The SELECT Statement 25 Retrieving Individual Columns 25 Retrieving Multiple Columns 27 Retrieving All Columns 29 Retrieving Distinct Rows 29 Limiting Results 31 Using Fully Qualified Table Names 32 Using Comments 33 Summary 34 Challenges 34 Chapter 5 Sorting Retrieved Data 35 Sorting Data 35 Sorting by Multiple Columns 37 Sorting by Column Position 38 Specifying Sort Direction 39 Summary 41 Challenges 42 Chapter 6 Filtering Data 43 Using the WHERE Clause 43 WHERE Clause Operators 44 Checking Against a Single Value 45 Checking for Nonmatches 46 Checking for a Range of Values 47 Checking for No Value 48 Summary 49 Challenges 49 Chapter 7 Advanced Data Filtering 51 Combining WHERE Clauses 51 Using the AND Operator 51 Using the OR Operator 52 Understanding the Order of Evaluation 53 Using the IN Operator 54 Using the NOT Operator 56 Summary 58 Challenges 58 Chapter 8 Using Wildcard Filtering 59 Using the LIKE Operator 59 The Percent Sign (%) Wildcard 60 The Underscore (_) Wildcard 61 Tips for Using Wildcards 63 Summary 63 Challenges 63 Chapter 9 Searching Using Regular Expressions 65 Understanding Regular Expressions 65 Using MySQL Regular Expressions 66 Basic Character Matching 66 Performing OR Matches 68 Matching One of Several Characters 68 Matching Ranges 70 Matching Special Characters 70 Matching Character Classes 72 Matching Multiple Instances 72 Anchors 74 Summary 75 Challenges 76 Chapter 10 Creating Calculated Fields 77 Understanding Calculated Fields 77 Concatenating Fields 78 Using Aliases 80 Performing Mathematical Calculations 81 Summary 83 Challenges 83 Chapter 11 Using Data Manipulation Functions 85 Understanding Functions 85 Using Functions 86 Text Manipulation Functions 86 Date and Time Manipulation Functions 88 Numeric Manipulation Functions 91 Summary 92 Challenges 92 Chapter 12 Summarizing Data 93 Using Aggregate Functions 93 The Avg() Function 94 The Count() Function 95 The Max() Function 96 The Min() Function 97 The Sum() Function 98 Aggregates on Distinct Values 99 Combining Aggregate Functions 100 Summary 101 Challenges 101 Chapter 13 Grouping Data 103 Understanding Data Grouping 103 Creating Groups 104 Filtering Groups 105 Grouping and Sorting 107 Combining Grouping and Data Summarization 109 SELECT Clause Ordering 110 Summary 110 Challenges 110 Chapter 14 Working with Subqueries 113 Understanding Subqueries 113 Filtering by Subquery 113 Using Subqueries As Calculated Fields 117 Summary 119 Challenges 119 Chapter 15 Joining Tables 121 Understanding Joins 121 Understanding Relational Tables 121 Why Use Joins? 122 Creating a Join 123 The Importance of the WHERE Clause 124 Inner Joins 127 Joining Multiple Tables 128 Summary 130 Challenges 130 Chapter 16 Creating Advanced Joins 133 Using Table Aliases 133 Using Different Join Types 134 Self-Joins 134 Natural Joins 136 Outer Joins 137 Using Joins with Aggregate Functions 138 Using Joins and Join Conditions 139 Summary 140 Challenges 140 Chapter 17 Combining Queries 141 Understanding Combined Queries 141 Creating Combined Queries 141 Using UNION 141 UNION Rules 143 Including or Eliminating Duplicate Rows 144 Sorting Combined Query Results 145 Summary 146 Challenges 146 Chapter 18 Full-Text Searching 147 Understanding Full-Text Searching 147 Using Full-Text Searching 148 Performing Full-Text Searches 148 Using Query Expansion 151 Boolean Text Searches 153 Full-Text Searching Notes 156 Summary 157 Challenges 157 Chapter 19 Inserting Data 159 Understanding Data Insertion 159 Inserting Complete Rows 159 Inserting Multiple Rows 163 Inserting Retrieved Data 164 Summary 166 Challenges 166 Chapter 20 Updating and Deleting Data 167 Updating Data 167 Deleting Data 169 Guidelines for Updating and Deleting Data 170 Summary 171 Challenges 171 Chapter 21 Creating and Manipulating Tables 173 Creating Tables 173 Basic Table Creation 173 Working with NULL Values 175 Primary Keys Revisited 176 Using AUTO_INCREMENT 177 Specifying Default Values 178 Engine Types 179 Updating Tables 180 Deleting Tables 182 Renaming Tables 182 Summary 182 Challenges 182 Chapter 22 Using Views 183 Understanding Views 183 Why Use Views 184 View Rules and Restrictions 185 Using Views 185 Using Views to Simplify Complex Joins 185 Using Views to Reformat Retrieved Data 186 Using Views to Filter Unwanted Data 188 Using Views with Calculated Fields 188 Updating Views 189 Summary 190 Challenges 190 Chapter 23 Working with Stored Procedures 191 Understanding Stored Procedures 191 Why Use Stored Procedures 192 Using Stored Procedures 193 Executing Stored Procedures 193 Creating Stored Procedures 193 The DELIMITER Challenge 194 Dropping Stored Procedures 195 Working with Parameters 195 Building Intelligent Stored Procedures 199 Inspecting Stored Procedures 201 Summary 202 Challenges 202 Chapter 24 Using Cursors 203 Understanding Cursors 203 Working with Cursors 204 Creating Cursors 204 Opening and Closing Cursors 205 Using Cursor Data 206 Summary 210 Chapter 25 Using Triggers 211 Understanding Triggers 211 Creating Triggers 212 Dropping Triggers 213 Using Triggers 213 INSERT Triggers 213 DELETE Triggers 214 UPDATE Triggers 215 More on Triggers 216 Summary 216 Chapter 26 Managing Transaction Processing 217 Understanding Transaction Processing 217 Controlling Transactions 219 Using ROLLBACK 219 Using COMMIT 220 Using Savepoints 220 Changing the Default Commit Behavior 221 Summary 222 Chapter 27 Globalization and Localization 223 Understanding Character Sets and Collation Sequences 223 Working with Character Sets and Collation Sequences 224 Summary 226 Chapter 28 Managing Security 227 Understanding Access Control 227 Managing Users 228 Creating User Accounts 229 Deleting User Accounts 230 Setting Access Rights 230 Changing Passwords 233 Summary 234 Chapter 29 Database Maintenance 235 Backing Up Data 235 Performing Database Maintenance 235 Diagnosing Startup Problems 237 Reviewing Log Files 237 Summary 238 Chapter 30 Improving Performance 239 Improving Performance 239 Summary 240 Appendix A Getting Started with MySQL 241 What You Need 241 Obtaining the Software 242 Installing the Software 242 Preparing to Read This Book 242 Appendix B The Example Tables 243 Understanding the Example Tables 243 Table Descriptions 244 The vendors Table 244 The products Table 244 The customers Table 245 The orders Table 245 The orderitems Table 246 The productnotes Table 246 Creating the Sample Tables 247 Using Data Import 247 Using SQL Scripts 248 Appendix C MySQL Statement Syntax 249 ALTER TABLE 249 COMMIT 249 CREATE INDEX 250 CREATE PROCEDURE 250 CREATE TABLE 250 CREATE USER 250 CREATE VIEW 251 DELETE 251 DROP 251 INSERT 251 INSERT SELECT 251 ROLLBACK 252 SAVEPOINT 252 SELECT 252 START TRANSACTION 252 UPDATE 252 Appendix D MySQL Datatypes 253 String Datatypes 253 Numeric Datatypes 255 Date and Time Datatypes 256 Binary Datatypes 256 Appendix E MySQL Reserved Words 257 9780138223021 TOC 10/2/2023

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Book SynopsisTrade Review"You might think that museums are for collecting and laboratories for experimenting. Bruno J. Strasser tracks the creation of a hybrid culture--a 'way of knowing' that was comparative and experimental at the same time. Molecular biologists used the protein sequences of very various species to crack the genetic code. From bacteria to blood and protein to DNA, this engaging book restores collecting to the experimentalist tradition and gives 'big data' biology the history it needs."--Nick Hopwood, author of Haeckel's Embryos: Images, Evolution, and Fraud "Amidst all the hype surrounding Big Data and the life sciences, Bruno J. Strasser uncovers the deep continuities of collecting and comparing that link the latest data banks to venerable natural history museums. This bold book rethinks the relationship between field, laboratory, and archive, with important implications for the ethos of open publication in science."--Lorraine Daston, Max Planck Institute for the History of Science "The long-contested line between experimental life sciences and those that collect, compare, and classify is once more unsettled. It is now accepted that comparative sciences are open to experiment and always have been. And Bruno J. Strasser now argues that the celebrated achievements of experimental biology have similarly depended on practices of collecting and curating. And not just in our own new world of digital databases, but historically: from when experimenters first thought to make collecting forever obsolete. Strasser supports his bold revision with case studies of a broad range of sciences, from taxonomy to serology, experimental and then molecular biology, and bio-informatics. In its historical depth and breadth this is a benchmark book; and for all who want to know how life sciences really work, it's a must read."--Robert E. Kohler, University of Pennsylvania "A masterful, groundbreaking work: Strasser explores collecting activities in multiple branches of biology and medicine across several centuries, covering the territory from natural historical specimens to blood and proteins, and on to DNA sequences and contemporary big-data biology. His book assesses issues of lasting salience, including control of the collections, access to specimens and data, modes of publication, and assignment of authorship and credit. Strasser contends that big-data biology is not a sharp departure from the past but a hybrid, a joining of the experimentalist-reductionist inquiries into model organisms with the practices of collectors who classified and characterized their specimens and compared them with others. Strasser's research is wide and deep, his prose lucidly informative, and his analysis subtle, discerning, and persuasive." --Daniel J. Kevles, Yale University "Collecting Experiments is an exciting and welcome addition to the historiography of the long-standing debates about the changing roles of experimentation and description in the life sciences. Rejecting the older notion of an impassable dichotomy, Bruno J. Strasser suggests that the rise of experimental approaches to biology in the nineteenth century did not eclipse the more descriptive work of natural history, but rather became a part of an overall 'way of knowing' that included both approaches. 'Big data, ' whether obtained by experimental or observational methods had to be analyzed in the same manner. Strasser has done a great service to clarify the historical relationship between these two methodologies. It is a must for all scholars in the history of biology."--Garland Allen, Washington University in Saint Louis

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Book Synopsis

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Book SynopsisThis is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.Table of Contents Contents Preface Acknowledgements 1 Introduction 2 User Interfaces for Search by Marti Hearst 3 Modeling 4 Retrieval Evaluation 5 Relevance Feedback and Query Expansion 6 Documents: Languages & Properties with Gonzalo Navarro and Nivio Ziviani 7 Queries: Languages & Properties with Gonzalo Navarro 8 Text Classification with Marcos Gon¸calves 9 Indexing and Searching with Gonzalo Navarro 10 Parallel and Distributed IR with Eric Brown 11 Web Retrieval with Yoelle Maarek 12 Web Crawling with Carlos Castillo 13 Structured Text Retrieval with Mounia Lalmas 14 Multimedia Information Retrieval by Dulce Poncele´on and Malcolm Slaney 15 Enterprise Search by David Hawking 16 Library Systems by Edie Rasmussen 17 Digital Libraries by Marcos Gon¸calves A Open Source Search Engines with Christian Middleton B Biographies Bibliography Index

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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 SynopsisFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies.The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendationTable of ContentsGraphs in Social and Digital Media. Mathematical Preliminaries: Graphs and Matrices. Algebraic Graph Analysis. Web Search Based on Ranking. Label Propagation and Information Diffusion in Graphs. Graph-Based Pattern Classification and Dimensionality Reduction. Matrix and Tensor Factorization with Recommender System Applications. Multimedia Social Search Based on Hypergraph Learning. Graph Signal Processing in Social Media. Big Data Analytics for Social Networks. Semantic Model Adaptation for Evolving Big Social Data. Big Graph Storage, Processing and Visualization.

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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 SynopsisThere are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitionerâs viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.Table of ContentsIntroduction to Data Analysis. Understanding Sampling. Judgmental vs Statistical Sampling. Probability theory in Data Analysis. Types of Evidence. Population Analysis. Correlations and Regressions. Conducting the Audit. Obtaining Information from IT Systems for Analysis. Use of Computer Assisted Audit Techniques. Analysis of Big Data. Results Analysis and Validation. Root Cause Analysis. Data Analysis and Continuous Monitoring. Continuous Auditing. Financial Analysis. Excel and Data Analysis. ACL and Data Analysis. IDEA and Data Analysis. Analysis Reporting.

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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 SynopsisThis book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysTable of Contents1. Data, Stats and Stories - An Introduction 2. Python Programming Primer 3. Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & Pandas 4. The Measure of All Things - Statistics 5. Definitely Maybe: Probability and Distributions 6. Alluring Arguments and Ugly Facts - Statistical Modelling and Hypothesis Testing 7. Delightful Details - Data Visualisation 8. Dazzling Data Designs - Creating Charts A. Variance: Population v Sample B. Sum of First n Integers C. Sum of Squares of the First n Integers D. The Binomial Coefficient E. The Hypergeometric Distribution F. The Poisson Distribution G. The Normal Distribution H. Skewness and Kurtosis I. Kruskal-Wallis Test - No Ties

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Book SynopsisThe emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded suggests serious implications for learning and education.Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using real world examples' to motivate students to work with data, this book demonstrates how learners' relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially awareTable of Contents1. Introduction: Situating Data Science—Exploring How Relationships to Data Shape Learning 2. At Home with Data: Family Engagements with Data Involved in Type 1 Diabetes Management 3. Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration 4. Learning at the Intersection of Self and Society: The Family Geobiography as a Context for Data Science Education 5. Authoring Data Stories in a Media Makerspace: Adolescents Developing Critical Data Literacies 6. From Data Collectors to Data Producers: Shifting Students’ Relationship to Data, Lisa Hardy 7. Scripts and Counterscripts in Community-Based Data Science: Participatory Digital Mapping and the Pursuit of a Third Space 8. Learning to Reason with Data: How Did We Get Here and What Do We Know? 9. Educating Data Scientists and Data Literate Citizens for a New Generation of Data

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