Tabla de Contenidos: “…9.2.3 The Crank‐Nicholson Method -- 9.2.4 Using the MATLAB function 'pdepe()' -- 9.2.5 Two‐Dimensional Parabolic PDEs -- 9.3 Hyperbolic PDES -- 9.3.1 The Explicit Central Difference Method -- 9.3.2 Two‐Dimensional Hyperbolic PDEs -- 9.4 Finite Element Method (FEM) for Solving PDE -- 9.5 GUI of MATLAB for Solving PDES - PDEtool -- 9.5.1 Basic PDEs Solvable by PDEtool -- 9.5.2 The Usage of PDEtool -- 9.5.3 Examples of Using PDEtool to Solve PDEs -- Appendix A Mean Value Theorem -- Appendix B Matrix Operations/Properties -- B.1 Addition and Subtraction -- B.2 Multiplication -- B.3 Determinant -- B.4 Eigenvalues and Eigenvectors of a Matrix1 -- B.5 Inverse Matrix -- B.6 Symmetric/Hermitian Matrix -- B.7 Orthogonal/Unitary Matrix -- B.8 Permutation Matrix -- B.9 Rank -- B.10 Row Space and Null Space -- B.11 Row Echelon Form -- B.12 Positive Definiteness -- B.13 Scalar (Dot) Product and Vector (Cross) Product -- B.14 Matrix Inversion Lemma -- Appendix C Differentiation W.R.T. …”

Tabla de Contenidos: “…Cover -- About the Author -- Contents -- Preface -- Chapter 1: Introduction -- 1.1 Power System Studies -- 1.1.1 Network Modelling Stage -- 1.1.2 Mathematical Modelling Stage -- 1.1.3 Solution Stage -- 1.2 Organisation of Text Book -- 1.3 Computer's Role in Power System Studies -- 1.4 Matlab Fundamentals -- 1.4.1 Basics of MATLAB -- Chapter 2: Power System Network Matrices-1 -- 2.1 Introduction -- 2.2 Graph of a Power System Network -- 2.3 Definitions -- 2.3.1 Graph -- 2.3.2 Planar and Non-Planar Graphs -- 2.3.3 Rank of a Graph -- 2.3.4 Oriented Graph -- 2.3.5 Sub-Graph -- 2.3.6 Path -- 2.3.7 Connected Graph -- 2.3.8 Tree -- 2.3.9 Co-Tree -- 2.3.10 Basic Loops or Fundamental f -Loops -- 2.3.11 Basic Cutsets or Fundamental f -Cutsets -- 2.4 Incidence Matrices -- 2.4.1 Element Node Incidence Matrix (Â ) -- 2.4.2 Bus Incidence Matrix (A) -- 2.4.3 Branch Path Incidence Matrix (P) -- 2.4.4 Basic Cutset (or) Fundamental Cutset Incidence Matrix (C) -- 2.4.5 Augmented or Tie Cutset Incidence Matrix (C) -- 2.4.6 Basic or Fundamental f -loop Incidence Matrix (L) -- 2.4.7 Augmented Loop Incidence Matrix L -- 2.5 Primitive Network -- 2.5.1 Primitive Network in Impedance Form -- 2.5.2 Primitive Network in Admittance Form -- 2.6 Network Equations and Network Matrices -- 2.7 Bus Admittance Matrix -- 2.7.1 Direct Inspection Method -- 2.7.2 Step-by-Step Procedure -- 2.8 Network Matrices by Singular Transformation Method -- 2.8.1 Bus Admittance Matrix -- 2.8.2 Branch Admittance Matrix -- 2.8.3 Loop Impedance Matrix or Admittance Matrix -- 2.9 Network Matrices by Non-Singular Transformation Method -- 2.9.1 Branch Admittance Matrix -- 2.9.2 Loop Impedance and Loop Admittance Matrix -- 2.9.3 Bus Admittance and Bus Impedance Matrices -- 2.9.4 Algorithm for Singular and Non-Singular Transformation Methods -- Questions from Previous Question Papers…”

Tabla de Contenidos: “…7.5.3 Reflecting automation as a priority with your staff -- 7.5.4 Providing time for training and learning -- 7.6 Filling the skill-set gap -- 7.6.1 But if I build it, I own it -- 7.6.2 Building the new skill set -- 7.7 Approaching automation -- 7.7.1 Safety in tasks -- 7.7.2 Designing for safety -- 7.7.3 Complexity in tasks -- 7.7.4 How to rank tasks -- 7.7.5 Automating simple tasks -- 7.7.6 Automating complicated tasks -- 7.7.7 Automating complex tasks -- Summary -- 8 Off-hour deployments -- 8.1 War story -- 8.2 The layers of a deployment -- 8.3 Making deployments routine affairs -- 8.3.1 Accurate preproduction environments -- 8.3.2 Staging will never be exactly like production -- 8.4 Frequency reduces fear -- 8.5 Reducing fear by reducing risk -- 8.6 Handling failure in the layers of the deployment process -- 8.6.1 Feature flags -- 8.6.2 When to toggle off your feature flag -- 8.6.3 Fleet rollbacks -- 8.6.4 Deployment artifact rollbacks -- 8.6.5 Database-level rollbacks -- 8.7 Creating deployment artifacts -- 8.7.1 Leveraging package management -- 8.7.2 Configuration files in packages -- 8.8 Automating your deployment pipeline -- 8.8.1 Safely installing the new application -- Summary -- 9 Wasting a perfectly good incident -- 9.1 The components of a good postmortem -- 9.1.1 Creating mental models -- 9.1.2 Following the 24-hour rule -- 9.1.3 Setting the rules of the postmortem -- 9.2 The incident -- 9.3 Running the postmortem -- 9.3.1 Choosing whom to invite to the postmortem -- 9.3.2 Running through the timeline -- 9.3.3 Defining action items and following up -- 9.3.4 Documenting your postmortem -- 9.3.5 Sharing the postmortem -- Summary -- 10 Information hoarding: Only Brent knows -- 10.1 Understanding how information hoarding happens -- 10.2 Recognizing unintentional hoarders -- 10.2.1 Documentation isn't valued…”

Tabla de Contenidos: “…Intro -- Foundations of Linear and Generalized Linear Models -- Contents -- Preface -- Purpose of this book -- Use as a textbook -- Acknowledgments -- 1 Introduction to Linear and Generalized Linear Models -- 1.1 Components of a Generalized Linear Model -- 1.1.1 Random Component of a GLM -- 1.1.2 Linear Predictor of a GLM -- 1.1.3 Link Function of a GLM -- 1.1.4 A GLM with Identity Link Function is a "Linear Model" -- 1.1.5 GLMs for Normal, Binomial, and Poisson Responses -- 1.1.6 Advantages of GLMs versus Transforming the Data -- 1.2 Quantitative/Qualitative Explanatory Variables and Interpreting Effects -- 1.2.1 Quantitative and Qualitative Variables in Linear Predictors -- 1.2.2 Interval, Nominal, and Ordinal Variables -- 1.2.3 Interpreting Effects in Linear Models -- 1.3 Model Matrices and Model Vector Spaces -- 1.3.1 Model Matrices Induce Model Vector Spaces -- 1.3.2 Dimension of Model Space Equals Rank of Model Matrix -- 1.3.3 Example: The One-Way Layout -- 1.4 Identifiability and Estimability -- 1.4.1 Identifiability of GLM Model Parameters -- 1.4.2 Estimability in Linear Models -- 1.5 Example: Using Software to Fit a GLM -- 1.5.1 Example: Male Satellites for Female Horseshoe Crabs -- 1.5.2 Linear Model Using Weight to Predict Satellite Counts -- 1.5.3 Comparing Mean Numbers of Satellites by Crab Color -- Chapter Notes -- Exercises -- 2 Linear Models: Least Squares Theory -- 2.1 Least Squares Model Fitting -- 2.1.1 The Normal Equations and Least Squares Solution -- 2.1.2 Hat Matrix and Moments of Estimators -- 2.1.3 Bivariate Linear Model and Regression Toward the Mean -- 2.1.4 Least Squares Solutions When X Does Not Have Full Rank -- 2.1.5 Orthogonal Subspaces and Residuals -- 2.1.6 Alternatives to Least Squares -- 2.2 Projections of Data Onto Model Spaces -- 2.2.1 Projection Matrices -- 2.2.2 Projection Matrices for Linear Model Spaces…”

Tabla de Contenidos: “…Intro -- Contents -- Editors -- List of Contributors -- Chemicals Alternatives Assessment (CAA): Tools for Selecting Less Hazardous Chemicals -- 1.1 Introduction to Chemicals Alternatives Assessments -- 1.2 Common Traits Among CAA Paradigms -- 1.2.1 Step One: Hazard Assessment Through Literature Search and Data Identification -- 1.2.2 Step Two: Hazard Classification and Benchmarking of Relevant Data -- 1.2.3 Step Three: CAA Report Preparation -- 1.3 Life-cycle Assessment and Chemicals Alternatives Assessment -- 1.4 Chemical Alternatives Assessment Paradigms in Use: a Critical Evaluation -- 1.4.1 US EPA's Design for the Environment (DfE) -- 1.4.2 CPA's GreenScreenTM -- 1.4.3 Cradle to Cradle® (C2C) -- 1.4.4 TURI's Pollution Prevention Options Analysis System (P2OASys) -- 1.4.5 The Chemical Scoring and Ranking Assessment Model (SCRAM) -- 1.4.6 Chemicals Assessment and Ranking System (CARS) -- 1.4.7 SC Johnson &amp -- Son's GreenlistTM -- 1.4.8 PRIO -- 1.4.9 The Quick Scan -- 1.4.10 The Column Model and GHS Column Model -- 1.4.11 Evaluation Matrix -- 1.5 Challenges Facing Chemicals Alternatives Assessment Methods -- 1.5.1 Chemicals Alternatives Assessments and Data Gaps -- 1.6 Conclusion -- References -- European Initiatives for Selecting Sustainable Flame Retardants -- 2.1 Introduction - What are Flame Retardants? …”

Tabla de Contenidos: “…127 Ode: O Bosky Brook 128 Perdidi Diem 130 Sense and Conscience 132 'In deep and solemn dreams' 140 Lines on Cambridge of 1830 143 A Fragment [Where is the Giant of the Sun] 144 'O wake ere I grow jealous of sweet Sleep' 145 'The constant spirit of the world exults' 146 Sonnet [When that rank heat of evil's tropic day] 151 Sonnet [There are three things which fill my heart with sighs] 153 The Lover's Tale 155 'My life is full of weary days' 158 'If I were loved, as I desire to be' 159* The Lady of Shalott 160* Mariana in the South 161 Eleänore 162 The Miller's Daughter 163* Fatima 164* Œnone 166* To -- With the Following Poem [The Palace of Art] 167* The Palace of Art 169 The Hesperides 170* The Lotos-Eaters 171 Rosalind 172 'My Rosalind, my Rosalind' 173* A Dream of Fair Women 174 Song [Who can say] 175 Margaret 176 Kate 179 To -- [As when with downcast eyes] 185 Sonnet [Alas! …”

Tabla de Contenidos: “…Peripheral Content -- Audit 2: Brand Guidelines Audit -- Audit 3: Heuristic Audit -- Good Governance Guidelines -- Takeaways From the Success Layer -- Section III: Social Business Strategies and Tactics -- 8: Social Business Strategy for Marketing -- Appropriate Campaign Goals -- Content Marketing and Paid Media Amplification -- Modern Enterprise Search Marketing -- Case Study: Fort Collins Startup Week Goes Global on 2,500 -- Search Engine Optimization (Seo) -- Key Determinants Impacting Your Organic Search Ranking -- Enterprise Paid Search -- Predictive Advertising Management -- GEO Targeting and Ip-Based Advertising -- 9: Social Business Strategy for Sales -- What Is Social Selling? …”

Tabla de Contenidos: “…Authentication UI and Validating Inputs -- Installing Required Modules -- Setting Up Auth Pages -- Using Joi for Input Validation -- Setting Up User Authentication with Supabase -- Creating the Signup Function -- Creating and Managing Personal User Posts -- Adding RLS Policy for the 'Posts' Table -- Using Supabase API for Post Management -- Building the Post Information Page -- Displaying User Posts -- Summary -- Practice Questions -- Further Reading -- Chapter 6: Enhancing a Recipe-Sharing Site While Optimizing SEO Using Nuxt 3 -- Technical requirements -- Essential background - Understanding Search Engine Optimization (SEO) -- Exploring the repository -- Defining metadata for each page -- Setting up the project structure -- Setting up site configuration with nuxt SEO -- Overriding metadata -- Implementing structured data for improved search engine rankings -- Creating a recipe node -- Optimizing recipe images to load pages faster -- Creating a custom og:image for the recipe page -- Generating dynamic site maps for the recipe-sharing site -- Summary -- Practice questions -- Further reading -- Chapter 7: Building a Quiz Game Application to Learn about Testing -- Technical requirements -- Exploring the repository -- Essential background - fundamentals of testing -- Implementing the quiz game app -- Questions data -- Types definitions -- Quiz Pinia store -- The app.vue file -- Writing unit tests for the Pinia store -- Exploring Vitest -- Testing the quiz store -- Writing unit tests for components -- Setting up for component testing -- Creating the Question.spec.ts test file -- Writing the component unit test -- Writing end-to-end tests for the quiz game -- Setting up for E2E testing -- Writing E2E tests -- Exploring Vitest UI and test coverage tools -- Summary -- Practice questions -- Further reading…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Brief Contents -- Contents -- Examples and Applications -- Preface -- Part I: The Linear Regression Model -- CHAPTER 1 Econometrics -- 1.1 Introduction -- 1.2 The Paradigm of Econometrics -- 1.3 The Practice of Econometrics -- 1.4 Microeconometrics and Macroeconometrics -- 1.5 Econometric Modeling -- 1.6 Plan of the Book -- 1.7 Preliminaries -- 1.7.1 Numerical Examples -- 1.7.2 Software and Replication -- 1.7.3 Notational Conventions -- CHAPTER 2 The Linear Regression Model -- 2.1 Introduction -- 2.2 The Linear Regression Model -- 2.3 Assumptions of the Linear Regression Model -- 2.3.1 Linearity of the Regression Model -- 2.3.2 Full Rank -- 2.3.3 Regression -- 2.3.4 Homoscedastic and Nonautocorrelated Disturbances -- 2.3.5 Data Generating Process for the Regressors -- 2.3.6 Normality -- 2.3.7 Independence and Exogeneity -- 2.4 Summary and Conclusions -- CHAPTER 3 Least Squares Regression -- 3.1 Introduction -- 3.2 Least Squares Regression -- 3.2.1 The Least Squares Coefficient Vector -- 3.2.2 Application: An Investment Equation -- 3.2.3 Algebraic Aspects of the Least Squares Solution -- 3.2.4 Projection -- 3.3 Partitioned Regression and Partial Regression -- 3.4 Partial Regression and Partial Correlation Coefficients -- 3.5 Goodness of Fit and the Analysis of Variance -- 3.5.1 The Adjusted R-Squared and a Measure of Fit -- 3.5.2 R-Squared and the Constant Term in the Model -- 3.5.3 Comparing Models -- 3.6 Linearly Transformed Regression -- 3.7 Summary and Conclusions -- CHAPTER 4 Estimating the Regression Model by Least Squares -- 4.1 Introduction -- 4.2 Motivating Least Squares -- 4.2.1 Population Orthogonality Conditions -- 4.2.2 Minimum Mean Squared Error Predictor -- 4.2.3 Minimum Variance Linear Unbiased Estimation -- 4.3 Statistical Properties of the Least Squares Estimator…”

Tabla de Contenidos:

Tabla de Contenidos: “…8.1 Banach and Hilbert space rank, boundedness, and Schauder bases -- 8.2 Commutative and noncommutative Banach algebras -- 8.3 Subgroup in a Banach algebra -- 8.4 Bounded operators on a Hilbert space -- 8.5 Invertible operator algebra criteria on a Hilbert space -- 8.6 Spectrum in a Banach algebra -- 8.7 Ideals in a Banach algebra -- 8.8 Gelfand-Naimark-Segal construction -- 8.9 Generating a C* algebra -- 8.10 The Gelfand formula -- References -- 9 Von Neumann algebra -- 9.1 Operator topologies -- 9.2 Two basic von Neumann algebras -- 9.3 Commutant in a von Neumann algebra -- 9.4 The Gelfand transform -- References -- 10 Fiber bundles -- 10.1 MSA for the algebraic quotient spaces -- 10.2 The topological quotient space -- 10.3 Basic topological and manifold concepts -- 10.4 Fiber bundles from manifolds -- 10.5 Sections in a fiber bundle -- 10.6 Line and vector bundles -- 10.7 Analytic vector bundles -- 10.8 Elliptic curves over C -- 10.9 The quaternions -- 10.10 Hopf fibrations -- 10.11 Hopf fibration with bloch sphere S2, the one-qubit base -- 10.12 Hopf fibration with sphere S4, the two-qubit base -- References -- 11 Lie algebras and Lie groups -- 11.1 Algebraic structure -- 11.2 MSA view of a Lie algebra -- 11.3 Dimension of a Lie algebra -- 11.4 Ideals in a Lie algebra -- 11.5 Representations and MSA of a Lie group of a Lie algebra -- 11.6 Briefing on topological manifold properties of a Lie group -- 11.7 Formal description of matrix Lie groups -- 11.8 Mappings between Lie groups and Lie algebras -- 11.9 Complexification of Lie algebras -- References -- 12 Fundamental and universal covering groups -- 12.1 Homotopy a graphical view -- 12.2 Initial point equivalence for loops -- 12.3 MSA description of the fundamental group -- 12.4 Illustrating the fundamental group -- 12.5 Homotopic equivalence for topological spaces…”

Tabla de Contenidos: “…9.1.3 Why Testing Assumptions Can Be Unsatisfactory -- 9.1.4 Interpreting Student's T When It Rejects -- 9.1.5 Dealing with Unequal Variances: Welch's Test -- 9.1.6 R Function t.test -- 9.1.7 Student's T versus Welch's Test -- 9.1.8 The Impact of Outliers When Comparing Means -- 9.2 Comparing Medians -- 9.2.1 A Method Based on the McKean-Schrader Estimator -- 9.2.2 A Percentile Bootstrap Method -- 9.2.3 R Functions msmed, medpb2, split, and fac2list -- 9.2.4 An Important Issue: The Choice of Method can Matter -- 9.3 Comparing Trimmed Means -- 9.3.1 R Functions yuen, yuenbt, and trimpb2 -- 9.3.2 Skipped Measures of Location and Deleting Outliers -- 9.3.3 R Function pb2gen -- 9.4 Tukey's Three-Decision Rule -- 9.5 Comparing Variances -- 9.5.1 R Function comvar2 -- 9.6 Rank-Based (Nonparametric) Methods -- 9.6.1 Wilcoxon-Mann-Whitney Test -- 9.6.2 R Function wmw -- 9.6.3 Handling Heteroscedasticity -- 9.6.4 R Functions cid and cidv2 -- 9.7 Measuring Effect Size -- 9.7.1 Cohen's d -- 9.7.2 Concerns about Cohen's d and How They Might Be Addressed -- 9.7.3 R Functions akp.effect, yuenv2, and med.effect -- 9.8 Plotting Data -- 9.8.1 R Functions ebarplot, ebarplot.med, g2plot, and boxplot -- 9.9 Comparing Quantiles -- 9.9.1 R Function qcomhd -- 9.10 Comparing Two Binomial Distributions -- 9.10.1 Improved Methods -- 9.10.2 R Functions twobinom and twobicipv -- 9.11 A Method for Discrete or Categorical Data -- 9.11.1 R Functions disc2com, binband, and splotg2 -- 9.12 Comparing Regression Lines -- 9.12.1 Classic ANCOVA -- 9.12.2 R Function CLASSanc -- 9.12.3 Heteroscedastic Methods for Comparing the Slopes and Intercepts -- 9.12.4 R Functions olsJ2 and ols2ci -- 9.12.5 Dealing with Outliers among the Dependent Variable -- 9.12.6 R Functions reg2ci, ancGpar, and reg2plot -- 9.12.7 A Closer Look at Comparing Nonparallel Regression Lines -- 9.12.8 R Function ancJN…”

Tabla de Contenidos: “…13.5 Mapping from Copula Space to Data Space -- 13.6 Multi-dimensional Data and Copulas -- 13.7 Further Insight into the Gaussian Copula: A Non-rigorous View -- 13.8 The Real Power of Copulas -- 13.9 General Method of Fitting Distributions and Simulations - A Copula Approach -- 13.9.1 Fitting the Model -- 13.9.2 Simulating Data Using the mvdc and rMvdc Functions -- 13.10 How Non-Gaussian Copulas Can Improve Modelling -- 13.11 Tail Correlations -- 13.12 Exercise (Challenging) -- 13.13 Appendix 1 - Copula Properties -- 13.14 Appendix 2 - Rank Correlation and Kendall's Tau, -- 13.15 Recommended Reading -- 14 Copulas - A Modelling Exercise -- 14.1 Introduction -- 14.2 Modelling Future Claims -- 14.2.1 Data -- 14.2.2 Fitting Appropriate Marginal Distributions -- 14.2.3 Fitting The Copula -- 14.2.4 Assessing Risk From the Analysis of Simulated Values -- 14.2.5 Comparison with the Gaussian Copula Model -- 14.2.6 Comparison of the Models with the Data -- 14.3 Another Example: Banking Regulator -- 14.4 Conclusion -- 15 Bond Portfolio Valuation: A Simple Credit Risk Model -- 15.1 Introduction -- 15.2 Our Example Bond Portfolio -- 15.2.1 Description -- 15.2.2 The Transition Matrix -- 15.2.3 Correlation Matrix -- 15.2.4 Simulations and Results -- 15.2.5 Incorporating Interest Rate Risk - A Simple Adjustment -- 15.2.6 Portfolio Consisting of Highly Correlated Bonds -- 15.3 Further Development of this Model -- 15.4 Recommended Reading -- 16 The Markov 2-State Mortality Model -- 16.1 Introduction -- 16.2 Markov 2-State Model -- 16.3 Simple Applications of the 2-State Model -- 16.4 Estimating Mortality Rates from Data -- 16.5 An Example: Calculating Mortality Rates for One Age Band -- 16.6 Uncertainty in Our Estimates -- 16.7 Next Steps? …”

Tabla de Contenidos: “…. -- 1.10.5 Digital Equipment Corp - DEC -- 1.11 Computer company revenue rankings -- 1.12 Computer structures book -- 1.13 Computer family architectures - CFA -- 1.14 Virtual memory and page replacement algorithms -- 1.15 Memory space fragmentation and dynamic storage allocation -- 1.15.1 Page replacement algorithms -- 1.15.2 Simplified analysis of a paging system -- 1.16 Analysis of thrashing in 2-phase locking - 2PL systems…”

Tabla de Contenidos: “…CART -- 18.5.4 Detailed Look at the Statistical Analysis -- 18.5.5 Early Results on Defect Data Sets -- 18.6 Summary -- Part IV: Sharing Models -- Chapter 19: Sharing Models: Challenges and Methods -- Chapter 20: Ensembles of Learning Machines -- 20.1 When and Why Ensembles Work -- 20.1.1 Intuition -- 20.1.2 Theoretical Foundation -- 20.2 Bootstrap Aggregating (Bagging) -- 20.2.1 How Bagging Works -- 20.2.2 When and Why Bagging Works -- 20.2.3 Potential Advantages of Bagging for SEE -- 20.3 Regression Trees (RTs) for Bagging -- 20.4 Evaluation Framework -- 20.4.1 Choice of Data Sets and Preprocessing Techniques -- 20.4.1.1 PROMISE data -- 20.4.1.2 ISBSG data -- 20.4.2 Choice of Learning Machines -- 20.4.3 Choice of Evaluation Methods -- 20.4.4 Choice of Parameters -- 20.5 Evaluation of Bagging+RTs in SEE -- 20.5.1 Friedman Ranking -- 20.5.2 Approaches Most Often Ranked First or Second in Terms of MAE, MMRE and PRED(25) -- 20.5.3 Magnitude of Performance Against the Best -- 20.5.4 Discussion -- 20.6 Further Understanding of Bagging+RTs in SEE -- 20.7 Summary -- Chapter 21: How to Adapt Models in a Dynamic World -- 21.1 Cross-Company Data and Questions Tackled…”

“…Business Intelligence is now being democratized by being shared with the rank and file employee demanding higher levels of RDBMS scalability and ease of use, being delivered through Web portals. …”

Tabla de Contenidos: “…-- Nvidia GPU support optimized -- Introducing TensorFlow Lite -- Eager execution -- Optimized Accelerated Linear Algebra (XLA) -- Installing and configuring TensorFlow -- TensorFlow computational graph -- TensorFlow code structure -- Eager execution with TensorFlow -- Data model in TensorFlow -- Tensor -- Rank and shape -- Data type -- Variables -- Fetches -- Feeds and placeholders -- Visualizing computations through TensorBoard -- How does TensorBoard work? …”

Tabla de Contenidos: “…Chapter 9: Time Series Analysis and Forecasting with MATLAB -- Technical requirements -- Exploring the basic concepts of time series data -- Understanding predictive forecasting -- Introducing forecasting methodologies -- Time series analysis -- Extracting statistics from sequential data -- Converting a dataset into a time series format in MATLAB -- Understanding time series slicing -- Resampling time series data in MATLAB -- Moving average -- Exponential smoothing -- Implementing a model to predict the stock market -- Dealing with imbalanced datasets in MATLAB -- Understanding oversampling -- Exploring undersampling -- Summary -- Chapter 10: MATLAB Tools for Recommender Systems -- Technical requirements -- Introducing the basic concepts of recommender systems -- Understanding CF -- Content-based filtering explained -- Hybrid recommender systems -- Finding similar users in data -- Creating recommender systems for network intrusion detection using MATLAB -- Recommender system for NIDS -- NIDS using a recommender system in MATLAB -- Deploying machine learning models -- Understanding model compression -- Discovering model pruning techniques -- Introducing quantization for efficient inference on edge devices -- Getting started with knowledge distillation -- Learning low-rank approximation -- Summary -- Chapter 11: Anomaly Detection in MATLAB -- Technical requirements -- Introducing anomaly detection and fault diagnosis systems -- Anomaly detection overview -- Fault diagnosis systems explained -- Approaching fault diagnosis using ML -- Using ML to identify anomalous functioning -- Anomaly detection using logistic regression -- Improving accuracy using the Random Forest algorithm -- Building a fault diagnosis system using MATLAB -- Understanding advanced regularization techniques -- Understanding dropout -- Exploring L1 and L2 regularization…”

Tabla de Contenidos: “…-- Benefits of keyword search -- Benefits of ranked results -- Solr use cases -- Social media -- Science and research -- Search engine -- E-commerce -- Media and entertainment -- Government -- Education -- What's new in Solr 7? …”

Tabla de Contenidos: “…Inhaltsverzeichnis 7 -- Tabellenverzeichnis 9 -- Abbildungsverzeichnis 12 -- Einführung 15 -- 1.1 Überlegungen zu lnfrastrukturleistungen 15 -- 1.2 Überlegungen zu Kollaborationsplattformen . 17 -- 1.3 Aufbau der Arbeit 21 -- Volkswirtschaftstheoretische Betrachtung . 23 -- 2.1 Wissen als ökonomische Kategorie 24 -- 2.2 Transparenz vs. lntransparenz . 27 -- 2.3 Ökonomie als evolutionärer Prozess 31 -- 2.4 Entropie 35 -- 2.5 Kontextualisierung und Bedeutungskoordination . 37 -- Überlegungen zu einem Transaktionssystemunterstützungsmodell. . 41 -- 3.1 Input-Output-Tabellen als produktionsbezogene Transaktionsdarstellung 47 -- 3.2 Zum Aufbau der Input-Output-Rechnung . 49 -- 3.2.1 Zuordnungsprinzipien und Aufstellungsprobleme 57 -- 3.2.2 Annahmen bei der Aufstellung von Input-Output-Tabellen 59 -- 3.2.3 Generierung von Güter x Güter IOT mit der Gütertechnologieannahme . 62 -- 3.2.4 Negative Werte im Zuge der Gütertechnologieannahme . 68 -- 3.3 Alternative Formen der Produktionsstrukturerhebung 73 -- BCI - Modell und Daten . 75 -- Datenaufbereitungsphase 83 -- 5.1 Profildaten . 83 -- 5.2 Transaktionsdaten 84 -- 5.3 Strukturdaten 86 -- 5.4 Abgeleitete Datenquellen 87 -- 5.4.1 ,Seil' -Matrizen . 88 -- 5.4.2 ,Buy'-Matrizen 91 -- 5.4.3 Erweiterungen der ,Buy'-Matrizen 93 -- 5.4.4 BCliot - Input-Output-Relationen auf der -- Kollaborationsplattform 97 -- Strukturanalysephase 101 -- 6.1 Graphentheoretische Grundlagen 101 -- 6.2 Einfache Verflechtungseigenschaften auf Basis der Graphentheorie 107 -- 6.2.1 Grade und Dichte . 108 -- 6.2.2 Erreichbarkeit und Distanzen . 11 O -- 6.2.3 Indexierte Knoteneigenschaften . 112 -- 6.3 Knotenbetrachtung unter Einbezug direkter und indirekter -- Verflechtungen 116 -- 6.4 Berücksichtigung von Kantengewichten bei der Bewertung von Knoten 121 -- 6.4.1 Bedeutungsbestimmung mit der Leontief-lnverse 122 -- 6.4.2 Cliquenidentifikation und komplementärer Status nach Hubbell" 126 -- 6.4.3 Zwischen Perron-Frobenius und PageRankTM . 132 -- 6.5 Darstellung von Verflechtungsdaten . 135 -- 6.6 Blockmodeling - Clustering und Partitionierung von relationalen Datensätzen 137 -- 6.6.1 Direkte und indirekte Verfahren . 140 -- 6.6.2 Äquivalenzrelationen und Blocktypen 141 -- 6.6.3 Optimierungsprozess . 145 -- 6.6.4 Two-mode Blockmodeling 148 -- 6.6.5 Illustration des two-mode Blockmodeling . 150 -- 6.6.6 Blockmodellberechnung für gewichtete Relationen . 153 -- 6.6.7 Illustration der Blockmodellberechnung für gewichtete Kanten 156 -- 7 Strukturvergleichsphase 159 -- 7 .1 Lückentypologie 159 -- 7.2 Identifikation von direkten internen Lücken & potenziellen -- Performanzlücken . 162 -- 7.3 Identifikation von indirekten internen und externen Lücken 166 -- 7.4 Identifikation von internen Lückenfolgen 169 -- 7.5 Identifikation von externen Lückenfolgen . 173 -- 7.6 Deskriptive Auswertung der Lücken bzw. …”