Tabla de Contenidos: “…-- Have a go hero -- Receiving operator curves -- Time for action - ROC construction -- What just happened? -- Doing it in Python -- Logistic regression for the German credit screening dataset -- Time for action - logistic regression for the German credit dataset -- What just happened? …”

Tabla de Contenidos: “…-- Choosing a hidden layer architecture -- Coding the hidden layers for our example -- The output layer -- Putting it all together -- Training our model -- Using the checkpoint callback in Keras -- Measuring ROC AUC in a custom callback -- Measuring precision, recall, and f1-score -- Summary -- Chapter 5: Using Keras to Solve Multiclass Classification Problems -- Multiclass classification and deep neural networks -- Benefits -- Drawbacks -- Case study - handwritten digit classification -- Problem definition -- Model inputs and outputs -- Flattening inputs -- Categorical outputs -- Cost function -- Metrics -- Building a multiclass classifier in Keras -- Loading MNIST -- Input layer -- Hidden layers -- Output layer -- Softmax activation -- Putting it all together -- Training -- Using scikit-learn metrics with multiclass models -- Controlling variance with dropout…”

Tabla de Contenidos: “…5.3.3 Saliency Map Computation using Object Representation 184 -- 5.3.4 Using Attention for Object Recognition 184 -- 5.3.5 Implementation 185 -- 5.3.6 Optimizing the Selection of Top-down Bias 186 -- 5.4 Attention with Memory of Learning and Amnesic Function 187 -- 5.4.1 Visual Memory: Amnesic IHDR Tree 188 -- 5.4.2 Competition Neural Network Under the Guidance of Amnesic IHDR 191 -- 5.5 Top-down Computation in the Visual Attention System: VOCUS 193 -- 5.5.1 Bottom-up Features and Bottom-up Saliency Map 193 -- 5.5.2 Top-down Weights and Top-down Saliency Map 194 -- 5.5.3 Global Saliency Map 196 -- 5.6 Hybrid Model of Bottom-up Saliency with Top-down Attention Process 196 -- 5.6.1 Computation of the Bottom-up Saliency Map 197 -- 5.6.2 Learning of Fuzzy ART Networks and Top-down Decision 197 -- 5.7 Top-down Modelling in the Bayesian Framework 199 -- 5.7.1 Review of Basic Framework 200 -- 5.7.2 The Estimation of Conditional Probability Density 201 -- 5.8 Summary 202 -- References 202 -- 6 Validation and Evaluation for Visual Attention Models 207 -- 6.1 Simple Man-made Visual Patterns 207 -- 6.2 Human-labelled Images 208 -- 6.3 Eye-tracking Data 209 -- 6.4 Quantitative Evaluation 211 -- 6.4.1 Some Basic Measures 211 -- 6.4.2 ROC Curve and AUC Score 213 -- 6.4.3 Inter-subject ROC Area 213 -- 6.5 Quantifying the Performance of a Saliency Model to Human Eye Movement in Static and Dynamic Scenes 215 -- 6.6 Spearman's Rank Order Correlation with Visual Conspicuity 217 -- References 219 -- PART III APPLICATIONS OF ATTENTION SELECTION MODELS 221 -- 7 Applications in Computer Vision, Image Retrieval and Robotics 223 -- 7.1 Object Detection and Recognition in Computer Vision 224 -- 7.1.1 Basic Concepts 224 -- 7.1.2 Feature Extraction 224 -- 7.1.3 Object Detection and Classification 227 -- 7.2 Attention Based Object Detection and Recognition in a Natural Scene 231 -- 7.2.1 Object Detection Combined with Bottom-up Model 231 -- 7.2.2 Object Detection based on Attention Elicitation 233.…”

“…Help employees understand the career-enhancing aspects of change How to maximize your organization’s ROC (return on change). Most of the activity related to change management focuses on successfully implementing individual projects. …”

Tabla de Contenidos: “…4.7.3 Gamma GLMs for House Selling Price Data -- Appendix: GLM Analogs of Orthogonality Results for Linear Models -- Chapter Notes -- Exercises -- 5 Models for Binary Data -- 5.1 Link Functions for Binary Data -- 5.1.1 Ungrouped versus Grouped Binary Data -- 5.1.2 Latent Variable Threshold Model for Binary GLMs -- 5.1.3 Probit, Logistic, and Linear Probability Models -- 5.2 Logistic Regression: Properties and Interpretations -- 5.2.1 Interpreting : Effects on Probabilities and on Odds -- 5.2.2 Logistic Regression with Case-Control Studies -- 5.2.3 Logistic Regression is Implied by Normal Explanatory Variables -- 5.2.4 Summarizing Predictive Power: Classification Tables and ROC Curves -- 5.2.5 Summarizing Predictive Power: Correlation Measures -- 5.3 Inference About Parameters of Logistic Regression Models -- 5.3.1 Logistic Regression Likelihood Equations -- 5.3.2 Covariance Matrix of Logistic Parameter Estimators -- 5.3.3 Statistical Inference: Wald Method is Suboptimal -- 5.3.4 Conditional Logistic Regression to Eliminate Nuisance Parameters -- 5.4 Logistic Regression Model Fitting -- 5.4.1 Iterative Fitting of Logistic Regression Models -- 5.4.2 Infinite Parameter Estimates in Logistic Regression -- 5.5 Deviance and Goodness of Fit for Binary GLMS -- 5.5.1 Deviance and Pearson Goodness-of-Fit Statistics -- 5.5.2 Chi-Squared Tests of Fit and Model Comparisons -- 5.5.3 Residuals: Pearson, Deviance, and Standardized -- 5.5.4 Influence Diagnostics for Logistic Regression -- 5.6 Probit and Complementary Log-Log Models -- 5.6.1 Probit Models: Interpreting Effects -- 5.6.2 Probit Model Fitting -- 5.6.3 Log-Log and Complementary Log-Log Link Models -- 5.7 Examples: Binary Data Modeling -- 5.7.1 Example: Risk Factors for Endometrial Cancer Grade -- 5.7.2 Example: Dose-Response Study -- Chapter Notes -- Exercises -- 6 Multinomial Response Models…”

Tabla de Contenidos: “…4.4.2 Experimental Design for A/B Tests -- 4.4.3 Setting Up A/B Tests: A Step-by-Step Guide -- 4.4.4 Statistical Significance in A/B Tests -- 4.4.5 Advanced A/B Testing Techniques -- 4.4.6 Potential Pitfalls in A/B Testing -- 4.4.7 Interpreting A/B Test Results -- 4.5 Hypothesis Testing in Marketing -- 4.5.1 Introduction to Hypothesis Testing -- 4.5.2 Common Hypothesis Tests in Marketing -- 4.5.3 Significance Levels and P-Values -- 4.6 Customer Segmentation and Processing -- 4.6.1 K-Means Clustering -- 4.6.2 Hierarchical Clustering in Customer Segmentation -- 4.6.3 Recency, Frequency, Monetary Analysis in Marketing -- 4.7 Practical Examples: Inferential Analytics for Customer Segmentation and Hypothesis Testing for Marketing Campaign Performance -- 4.7.1 Inferential Analytics for Customer Segmentation -- 4.7.2 Hypothesis Testing for Marketing Campaign Performance -- 4.8 Conclusion -- 4.9 References -- Chapter 5 Predictive Analytics and Machine Learning -- 5.1 Introduction -- 5.1.1 Overview of Predictive Analytics -- 5.1.2 Machine Learning in Marketing -- 5.1.3 Common Challenges in Predictive Analytics and Machine Learning in Marketing -- 5.1.4 Misconceptions in Predictive Analytics and Machine Learning in Marketing -- 5.2 Predictive Analytics Techniques -- 5.2.1 Linear and Logistic Regression -- 5.2.2 Time Series Forecasting -- 5.3 Machine Learning Techniques -- 5.3.1 Supervised Learning for Marketing -- 5.3.2 Unsupervised Learning for Marketing -- 5.3.3 Reinforcement Learning for Marketing -- 5.4 Model Evaluation and Selection -- 5.4.1 Model Accuracy, Precision, and Recall -- 5.4.2 ROC Curves and AUC -- 5.4.3 Cross-Validation Techniques -- 5.4.4 Model Complexity and Overfitting -- 5.5 Churn Prediction, Customer Lifetime Value, and Propensity Modeling -- 5.5.1 Understanding Churn and Its Importance -- 5.5.2 CLV Computation and Applications…”