Tabla de Contenidos: “…Conté: Soy el que soy : el Dios de la Biblia -- Dios creador : hombre, imagen y aliado de Dios -- Israel, pueblo de la palabra : patriarcas y éxodo -- Moisés : liberador, hombre de la alianza -- Así dice Yahvé : profetas preexílicos -- Ezequiel e Isaías II : Palabra de Dios en la gran crisis -- Profetas anteriores : la teología se hace historia -- Hija de Sion, los último profetas -- Libro de las fiestas, una teología celebrativa -- Biblia de mujeres : historias subversivas -- Biblia de la sabiduría, pensar a Dios -- Biblia apocalíptica : el último tiempo (Daniel) -- Jesús histórico y Cristo de la fe -- Denuncia profética : Reino de Dios, contra Belcebú y Mammón -- Proyecto mesiánico : el signo del Hijo del Hombre -- Era necesario : matar a Jesús, negar la Palabra -- Resurrección : toda la teología -- ¿No he visto al Señor? …”

Tabla de Contenidos: “…9.1 Introduction -- 9.2 Applications of an ANOVA Quantile Regression -- 9.2.1 One‐Way ANOVA‐QR -- 9.2.2 Two‐Way ANOVA Quantile Regression -- 9.2.2.1 The Simplest Equation of Two‐Way ANOVA‐QR -- 9.2.2.2 A Special Equation of the Two‐Way ANOVA‐QR -- 9.2.2.3 An Additive Two‐Way ANOVA‐QR -- 9.2.3 Three‐Way ANOVA‐QRs -- 9.3 Quantile Regressions with Numerical Predictors -- 9.3.1 QR of LWAGE on GRADE -- 9.3.1.1 A Polynomial QR of LWAGE on GRADE -- 9.3.1.2 The Simplest Linear QR of Y1 on a Numerical X1 -- 9.3.2 Quantile Regressions of Y1 on (X1,X2) -- 9.3.2.1 Hierarchical and Nonhierarchical Two‐Way Interaction QRs -- 9.3.2.2 A Special Polynomial Interaction QR -- 9.3.2.3 A Double Polynomial Interaction QR of Y1 on (X1,X2) -- 9.3.3 QRs of Y1 on Numerical Variables (X1,X2,X3) -- 9.3.3.1 A Full Two‐Way Interaction QR -- 9.3.3.2 A Full‐Three‐Way‐Interaction QR -- 9.4 Heterogeneous Quantile‐Regressions -- 9.4.1 Heterogeneous Quantile Regressions by a Factor -- 9.4.1.1 A Heterogeneous Linear QR of LWAGE on POTEXP by IND1 -- 9.4.1.2 A Heterogeneous Third‐Degree Polynomial QR of LWAGE on GRADE -- 9.4.1.3 An Application of QR for a Large Number of Groups -- 9.4.1.4 Comparison Between Selected Heterogeneous QR(Median) -- Chapter 10 Quantile Regressions of a Latent Variable -- 10.1 Introduction -- 10.2 Spearman‐rank Correlation -- 10.3 Applications of ANOVA‐QR(τ) -- 10.3.1 One‐way ANOVA‐QR of BLV -- 10.3.2 A Two‐Way ANOVA‐QR of BLV -- 10.3.2.1 The Simplest Equation of a Two‐Way ANOVA‐QR of BLV -- 10.3.2.2 A Two‐way ANOVA‐QR of BLV with an Intercept -- 10.3.2.3 A Special Equation of Two‐Way ANOVA‐QR of BLV -- 10.4 Three‐way ANOVA‐QR of BLV -- 10.5 QRs of BLV on Numerical Predictors -- 10.5.1 QRs of BLV on MW -- 10.5.1.1 The Simplest Linear Regression of BLV on MW -- 10.5.1.2 Polynomial Regression of BLV on MW -- 10.5.2 QRs of BLV on Two Numerical Predictors…”

Tabla de Contenidos: “…-- 4.3.2 Separation Property -- 4.3.3 Conditions for Existence of Estimator Gain H -- 4.3.4 Concept of Observability -- 4.3.5 Concept of Detectability -- 4.4 Optimal Control -- 4.4.1 Linear Quadratic Regulator (LQR) -- 4.4.2 Linear Quadratic Tracker (LQT) -- 4.4.2.1 LQT Without Direct Output Feedback -- 4.4.2.2 Robust LQT with Direct Output Feedback -- 4.4.2.3 Elementary Design Approach (Unstable!) …”

Tabla de Contenidos: “…Lloyd-Max algorithm; 1.2.3.2. Non-linear transformation; 1.2.3.3. Scale factor; 1.3. …”

Tabla de Contenidos: “…Algorithms and Networking for Computer Games; Contents; List of Figures; List of Tables; List of Algorithms; Preface; Acknowledgements; 1 Introduction; 1.1 Anatomy of Computer Games; 1.2 Synthetic Players; 1.2.1 Humanness; 1.2.2 Stance; 1.3 Multi-playing; 1.4 Games and Storytelling; 1.5 Other Game Design Considerations; 1.6 Outline of the Book; 1.6.1 Algorithms; 1.6.2 Networking; 1.7 Summary; Exercises; I Algorithms; 2 Random Numbers; 2.1 Linear Congruential Method; 2.1.1 Choice of parameters; 2.1.2 Testing the randomness; 2.1.3 Using the generators; 2.2 Discrete Finite Distributions…”

Tabla de Contenidos: “…PC Audio Editing: Broadcast, desktop and CD audio production; Copyright; Contents; Foreword; Preface; 1 Visual editing; 2 Some technical bits; 2.1 Loudness, decibels and frequencies; 2.2 Hearing safety; 2.3 Analogue and digital audio; 2.4 Time code; 3 Hardware and software requirements; 3.1 PC; 3.2 Sound card; 3.3 Loudspeakers/headphones; 3.4 Hard disks; 3.5 Universal serial bus; 3.6 Firewire; 3.7 Audio editors; 3.8 Linear editors; 3.9 Non-linear editors; 3.10 Multitrack; 3.11 Audio processing; 3.12 Mastering; 3.13 CD recording software; 3.14 DVD; 3.15 MIDI; 3.16 Control surface; 4 Recording…”

Tabla de Contenidos: “…2.3.2 Breadth-first search2.4 Shortest Paths; 2.4.1 Shortest path as a linear program; 2.4.2 Dijkstra's algorithm; 2.4.3 The Bellman-Ford algorithm; 2.4.4 The Floyd-Warshall algorithm; 2.5 Maximum Flows; 2.5.1 Maximum flow as a linear program; 2.5.2 The Ford-Fulkerson labeling algorithm; 2.5.3 Approximate maximum flow; 2.5.3.1 Electrical flows; 2.5.3.2 The algorithm; 2.6 Summary; 3 Advanced Flow Theory; 3.1 Multi-Terminal Flows; 3.1.1 The Gomory-Hu algorithm; 3.2 Minimum-Cost Flows; 3.2.1 Problem formulation; 3.2.2 The out-of-kilter algorithm; 3.2.3 Variants and modifications…”

Tabla de Contenidos: “…Inside Model.fit(): Dissecting gradient descent from example 1 -- 2.3. Linear regression with multiple input features -- 2.4. …”

Tabla de Contenidos: “…2.2.1 Image Formation by a Lens2.2.1.1 Imaging a Point Source; 2.2.1.2 Focal Length; 2.2.1.3 Numerical Aperture; 2.2.1.4 Lens Shape; 2.3 Diffraction-Limited Optical Systems; 2.3.1 Linear System Analysis; 2.4 Incoherent Illumination; 2.4.1 The Point Spread Function; 2.4.2 The Optical Transfer Function; 2.5 Coherent Illumination; 2.5.1 The Coherent Point Spread Function; 2.5.2 The Coherent Optical Transfer Function; 2.6 Resolution; 2.6.1 Abbe Distance; 2.6.2 Rayleigh Distance; 2.6.3 Size Calculations; 2.7 Aberration; 2.8 Calibration; 2.8.1 Spatial Calibration; 2.8.2 Photometric Calibration…”

Tabla de Contenidos: “…Conservation of Energy -- Problem Set (4/e): Conservation of Energy -- 9. Linear Momentum -- Problem Set (4/e): Linear Momentum -- 10. …”

Tabla de Contenidos: “…LA ESCUELA NUEVA Y LA CONSTRUCCIÓN DEL CONOCIMIENTO PSICOPEDAGÓGICO: EL INSTITUTO J. J. ROUSSEAU DE GINEBRA; 5. LA PRÁCTICA ESCOLAR EN LA ESCUELA NUEVA: RENOVACIÓN METODOLÓGICA; 6. …”

Tabla de Contenidos: “…Áreas y procesos del trabajo en el almacén susceptibles de generar conflictos: identificación y causas -- 11. …”

Tabla de Contenidos: “…4.1 Expresión4.2 Percepción y sentido estético; 4.3 Desarrollo de la sensibilidad y la creatividad; 4.4 Planteos para la enseñanza de la expresión grafoplástica en el Nivel Inicial; 4.5 La motivación; 4.6 Fines, objetivos y ejes básicos de las actividades grafoplásticas; 4.7 Conducción de actividades; 4.8 Sobre la evaluación; 4.9 Sobre las correcciones, ayudas e interferencias del maestro; 4.10 Ideas sueltas que pueden generar grandes proyectos; Actividades; Bibliografía utilizada y otra para seguir profundizando y consultando…”

Tabla de Contenidos: “…-- Metodología -- Conclusiones -- Capítulo VI -- Confianza y profesionalización de la empresa familiar -- Generar un "nuevo" modelo: empresa y consultor construyendo confianza -- Las Ocho edades del hombre postuladas por Erick Erickson…”

Tabla de Contenidos: “…12 Introducing deep learning for time series forecasting -- 12.1 When to use deep learning for time series forecasting -- 12.2 Exploring the different types of deep learning models -- 12.3 Getting ready to apply deep learning for forecasting -- 12.3.1 Performing data exploration -- 12.3.2 Feature engineering and data splitting -- 12.4 Next steps -- 12.5 Exercise -- Summary -- 13 Data windowing and creating baselines for deep learning -- 13.1 Creating windows of data -- 13.1.1 Exploring how deep learning models are trained for time series forecasting -- 13.1.2 Implementing the DataWindow class -- 13.2 Applying baseline models -- 13.2.1 Single-step baseline model -- 13.2.2 Multi-step baseline models -- 13.2.3 Multi-output baseline model -- 13.3 Next steps -- 13.4 Exercises -- Summary -- 14 Baby steps with deep learning -- 14.1 Implementing a linear model -- 14.1.1 Implementing a single-step linear model -- 14.1.2 Implementing a multi-step linear model -- 14.1.3 Implementing a multi-output linear model -- 14.2 Implementing a deep neural network -- 14.2.1 Implementing a deep neural network as a single-step model -- 14.2.2 Implementing a deep neural network as a multi-step model -- 14.2.3 Implementing a deep neural network as a multi-output model -- 14.3 Next steps -- 14.4 Exercises -- Summary -- 15 Remembering the past with LSTM -- 15.1 Exploring the recurrent neural network (RNN) -- 15.2 Examining the LSTM architecture -- 15.2.1 The forget gate -- 15.2.2 The input gate -- 15.2.3 The output gate -- 15.3 Implementing the LSTM architecture -- 15.3.1 Implementing an LSTM as a single-step model -- 15.3.2 Implementing an LSTM as a multi-step model -- 15.3.3 Implementing an LSTM as a multi-output model -- 15.4 Next steps -- 15.5 Exercises -- Summary -- 16 Filtering a time series with CNN -- 16.1 Examining the convolutional neural network (CNN)…”

Tabla de Contenidos: “…5.5.2 Biological Neuron Model -- 5.5.3 Artificial Neural Networks -- 5.5.4 Network Topologies -- 5.5.4.1 NARX Neural Network -- 5.5.5 ANN Modeling Using Mathematical Models -- 5.6 Neuron Spiking Model Using FitzHugh-Nagumo (F-N) System -- 5.6.1 Linearization of F-N System -- 5.6.2 Reduced Order Model of Linear System -- 5.6.3 Finite Difference Discretization of F-N System -- 5.6.4 MOR of F-N System Using POD-Galerkin Method -- 5.7 Ring Oscillator Model -- 5.7.1 Model Order Reduction of Ring Oscillator Circuit -- 5.7.2 Ring Oscillator Circuit Approximation Using Linear System MOR -- 5.7.3 POD-ANN Macromodel of Ring Oscillator Circuit -- 5.8 Nonlinear VLSI Interconnect Model Using Telegraph Equation -- 5.8.1 Macromodeling of VLSI Interconnect -- 5.8.2 Discretisation of Interconnect Model -- 5.8.3 Linearization of VLSI Interconnect Model -- 5.8.4 Reduced Order Linear Model of VLSI Interconnect -- 5.9 Macromodel Using Machine Learning -- 5.9.1 Activation Function -- 5.9.2 Bayesian Regularization -- 5.9.3 Optimization -- 5.10 MOR of Dynamical Systems Using POD-ANN -- 5.10.1 Accuracy and Performance Index -- 5.11 Numerical Results -- 5.11.1 F-N System -- 5.11.2 Ring Oscillator Model -- 5.11.3 Reduced Order POD Approximation of Ring Oscillator -- 5.11.3.1 Study of POD-ANN Approximation of Ring Oscillator for Variation in Amplitude of Input Signal and for Different Input Signals -- 5.11.3.2 POD-ANN Approximation of Ring Oscillator for Variation in Frequency -- 5.11.4 POD-ANN Approximation of VLSI Interconnect -- 5.12 Conclusion -- References -- Chapter 6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Material and Methods -- 6.4 Ensemble Classifiers -- 6.4.1 Bagging -- 6.4.1.1 Bagging Meta Estimator -- 6.4.1.2 Random Forest -- 6.4.2 Boosting -- 6.4.2.1 AdaBoost…”

Tabla de Contenidos: “…-- 4.8 Mesh Analysis of Circuits with Independent Current Sources -- 4.9 Mesh Analysis of Circuits Containing Dependent Sources -- 4.10 Summary -- 4.11 Problems -- Chapter 5 : Circuit Theorems -- 5.1 Linearity of a Circuit and Superposition Theorem -- 5.1.1 Linearity of a Circuit -- 5.2 Star-Delta Transformation Theorem -- 5.3 Substitution Theorem -- 5.4 Compensation Theorem -- 5.5 Thevenin's Theorem and Norton's Theorem -- 5.6 Determination of Equivalents for Circuits with Dependent Sources -- 5.7 Reciprocity Theorem -- 5.8 Maximum Power Transfer Theorem -- 5.9 Millman's Theorem -- 5.10 Summary -- 5.11 Problems -- Chapter 6 : Power and Energy in Periodic Waveforms -- 6.1 Why Sinusoids? …”

Tabla de Contenidos: “…Creating a PivotChart from a PivotTable -- Embedding a PivotChart on a PivotTable's worksheet -- Creating a PivotChart from an Excel table -- Working with PivotCharts -- Moving a PivotChart to another sheet -- Filtering a PivotChart -- Changing the PivotChart type -- Adding data labels to your PivotChart -- Sorting the PivotChart -- Adding PivotChart titles -- Moving the PivotChart legend -- Displaying a data table with the PivotChart -- Part 3 Discovering Advanced Data Analysis Tools -- Chapter 10 Tracking Trends and Making Forecasts -- Plotting a Best-Fit Trend line -- Calculating Best-Fit Values -- Plotting Forecasted Values -- Extending a Linear Trend -- Extending a linear trend using the fill handle -- Extending a linear trend using the Series command -- Calculating Forecasted Linear Values -- Plotting an Exponential Trend Line -- Calculating Exponential Trend Values -- Plotting a Logarithmic Trend Line -- Plotting a Power Trend Line -- Plotting a Polynomial Trend Line -- Chapter 11 Analyzing Data with Statistics -- Counting Things -- Counting numbers -- Counting nonempty cells -- Counting empty cells -- Counting cells that match criteria -- Counting cells that match multiple criteria -- Counting permutations -- Counting combinations -- Averaging Things -- Calculating an average -- Calculating a conditional average -- Calculating an average based on multiple conditions -- Calculating the median -- Calculating the mode -- Finding the Rank -- Determining the Nth Largest or Smallest Value -- Calculating the nth highest value -- Calculating the nth smallest value -- Creating a Grouped Frequency Distribution -- Calculating the Variance -- Calculating the Standard Deviation -- Finding the Correlation -- Chapter 12 Analyzing Data with Descriptive Statistics -- Loading the Analysis ToolPak -- Generating Descriptive Statistics…”

Tabla de Contenidos: “…4.5.2 RBM HTM -- 4.5.3 Pyragrid -- 4.6 Discussion -- 4.6.1 On-Chip Learning -- 4.6.2 Data Movement -- 4.6.3 Memory Requirements -- 4.6.4 Scalability -- 4.6.5 Network Lifespan -- 4.6.6 Network Latency -- 4.6.6.1 Parallelism -- 4.6.6.2 Pipelining -- 4.6.7 Power Consumption -- 4.7 Open Problems -- 4.8 Conclusion -- References -- Chapter 5 NLP-Based AI-Powered Sanskrit Voice Bot -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 Pipeline -- 5.3.1 Collect Data -- 5.3.2 Clean Data -- 5.3.3 Build Database -- 5.3.4 Install Required Libraries -- 5.3.5 Train and Validate -- 5.3.6 Test and Update -- 5.3.7 Combine All Models -- 5.3.8 Deploy the Bot -- 5.4 Methodology -- 5.4.1 Data Collection and Storage -- 5.4.1.1 Web Scrapping -- 5.4.1.2 Read Text from Image -- 5.4.1.3 MySQL Connectivity -- 5.4.1.4 Cleaning the Data -- 5.4.2 Various ML Models -- 5.4.2.1 Linear Regression and Logistic Regression -- 5.4.2.2 SVM - Support Vector Machine -- 5.4.2.3 PCA - Principal Component Analysis -- 5.4.3 Data Pre-Processing and NLP Pipeline -- 5.5 Results -- 5.5.1 Web Scrapping and MySQL Connectivity -- 5.5.2 Read Text from Image -- 5.5.3 Data Pre-Processing -- 5.5.4 Linear Regression -- 5.5.5 Linear Regression Using TensorFlow -- 5.5.6 Bias and Variance for Linear Regression -- 5.5.7 Logistic Regression -- 5.5.8 Classification Using TensorFlow -- 5.5.9 Support Vector Machines (SVM) -- 5.5.10 Principal Component Analysis (PCA) -- 5.5.11 Anomaly Detection and Speech Recognition -- 5.5.12 Text Recognition -- 5.6 Further Discussion on Classification Algorithms -- 5.6.1 Using Maximum Likelihood Estimator -- 5.6.2 Using Gradient Descent -- 5.6.3 Using Naive Bayes' Decision Theory -- 5.7 Conclusion -- Acknowledgment -- References -- Chapter 6 Automated Attendance Using Face Recognition -- 6.1 Introduction -- 6.2 All Modules Details -- 6.2.1 Face Detection Model…”

Tabla de Contenidos: “…Linear Programming Applications -- 8.1 Marketing Applications -- Media Selection -- Marketing Research -- 8.2 Manufacturing Applications -- Production Mix -- Production Scheduling -- 8.3 Employee Scheduling Applications -- Labor Planning…”