Tabla de Contenidos: “…Theory -- 1.2 Outline of the Book -- I Theoretical and Empirical Foundations -- 2 Financial Crises and Financial Instability: Definitions and Principles -- 2.1 A General Definition of Financial Crises -- 2.2 Asset Price Fluctuations and Aggregate Economic Activity -- 2.2.1 Determinants of Asset Prices -- 2.2.2 Asset Prices and Financial Constraints -- 2.2.2.1 Perfect Capital Market Theory -- 2.2.2.2 Imperfect Capital Market Theory -- 2.2.2.3 A Comparison with Real World Financial Constraints -- 2.2.3 Asset Prices and Aggregate Demand -- 2.2.4 Asset Prices, Liquidity, Solvency and the Emergence of Cumulative Processes -- 2.2.4.1 Liquidity, Solvency, and Profits: Definitions and Interdependencies -- 2.2.4.2 Determinants of Bankruptcy -- 2.2.4.3 Cumulative Expansions and Contractions -- 2.3 Determinants of Financial Instability -- 2.3.1 A General Definition of Financial Instability -- 2.3.2 Cash Flow Positions and Present Values -- 2.3.2.1 Hedge, Speculative and Ponzi-Finance -- 2.3.2.2 Financial Instability in Closed Economies -- 2.3.2.3 Foreign Hedge, Foreign Speculative, and Foreign Ponzi Finance -- 2.3.2.4 Financial Instability in Open Economies -- 2.3.3 Adequacy of Refinancing Possibilities -- 2.3.4 Excess Volatility in Asset Prices -- 2.3.5 Monetary Instability and Debt Deflation -- 2.4 Exogenous and Endogenous Financial Crises -- 3 Stylized Facts and Standard Theory of Financial Crises -- 3.1 Defining and Identifying Financial Crises -- 3.1.1 Currency Crises -- 3.1.2 Banking Crises -- 3.1.3 Twin Crises -- 3.2 Frequency and Severity of Financial Crises -- 3.2.1 Incidence of Financial Crises -- 3.2.2 Duration and Costs of Financial Crises -- 3.3 Business Cycles, Financial Liberalization, and Financial Crises -- 3.3.1 Basic Links…”

Tabla de Contenidos: “…12 ADVANCES IN DATA MINING 328 -- 12.1 Graph Mining 329 -- 12.2 Temporal Data Mining 343 -- 12.3 Spatial Data Mining (SDM) 357 -- 12.4 Distributed Data Mining (DDM) 360 -- 12.5 Correlation Does Not Imply Causality 369 -- 12.6 Privacy, Security, and Legal Aspects of Data Mining 376 -- 12.7 Review Questions and Problems 381 -- 12.8 References for Further Study 382 -- 13 GENETIC ALGORITHMS 385 -- 13.1 Fundamentals of GAs 386 -- 13.2 Optimization Using GAs 388 -- 13.3 A Simple Illustration of a GA 394 -- 13.4 Schemata 399 -- 13.5 TSP 402 -- 13.6 Machine Learning Using GAs 404 -- 13.7 GAs for Clustering 409 -- 13.8 Review Questions and Problems 411 -- 13.9 References for Further Study 413 -- 14 FUZZY SETS AND FUZZY LOGIC 414 -- 14.1 Fuzzy Sets 415 -- 14.2 Fuzzy-Set Operations 420 -- 14.3 Extension Principle and Fuzzy Relations 425 -- 14.4 Fuzzy Logic and Fuzzy Inference Systems 429 -- 14.5 Multifactorial Evaluation 433 -- 14.6 Extracting Fuzzy Models from Data 436 -- 14.7 Data Mining and Fuzzy Sets 441 -- 14.8 Review Questions and Problems 443 -- 14.9 References for Further Study 445 -- 15 VISUALIZATION METHODS 447 -- 15.1 Perception and Visualization 448 -- 15.2 Scientifi c Visualization and -- Information Visualization 449 -- 15.3 Parallel Coordinates 455 -- 15.4 Radial Visualization 458 -- 15.5 Visualization Using Self-Organizing Maps (SOMs) 460 -- 15.6 Visualization Systems for Data Mining 462 -- 15.7 Review Questions and Problems 467 -- 15.8 References for Further Study 468 -- Appendix A 470 -- A.1 Data-Mining Journals 470 -- A.2 Data-Mining Conferences 473 -- A.3 Data-Mining Forums/Blogs 477 -- A.4 Data Sets 478 -- A.5 Comercially and Publicly Available Tools 480 -- A.6 Web Site Links 489 -- Appendix B: Data-Mining Applications 496 -- B.1 Data Mining for Financial Data Analysis 496 -- B.2 Data Mining for the Telecomunications Industry 499 -- B.3 Data Mining for the Retail Industry 501 -- B.4 Data Mining in Health Care and Biomedical Research 503 -- B.5 Data Mining in Science and Engineering 506.…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Book 1 Defining Data Science -- Chapter 1 Considering the History and Uses of Data Science -- Considering the Elements of Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Understanding the role of programming -- Defining the Role of Data in the World -- Enticing people to buy products -- Keeping people safer -- Creating new technologies -- Performing analysis for research -- Providing art and entertainment -- Making life more interesting in other ways -- Creating the Data Science Pipeline -- Preparing the data -- Performing exploratory data analysis -- Learning from data -- Visualizing -- Obtaining insights and data products -- Comparing Different Languages Used for Data Science -- Obtaining an overview of data science languages -- Defining the pros and cons of using Python -- Defining the pros and cons of using R -- Learning to Perform Data Science Tasks Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Placing Data Science within the Realm of AI -- Seeing the Data to Data Science Relationship -- Considering the data architecture -- Acquiring data from various sources -- Performing data analysis -- Archiving the data -- Defining the Levels of AI -- Beginning with AI -- Advancing to machine learning -- Getting detailed with deep learning -- Creating a Pipeline from Data to AI -- Considering the desired output -- Defining a data architecture -- Combining various data sources -- Checking for errors and fixing them -- Performing the analysis -- Validating the result -- Enhancing application performance…”

Tabla de Contenidos: “…3.1 Enumeration of (s, f) Connected Path Sets -- 3.1.1 Method 1: Using Powers of Connection matrix -- 3.1.2 Method 2: Traversing Through Connection Matrix -- 3.1.3 Method 3: Using Incidence Matrix -- 3.2 Enumeration of All-node Connected Path Sets: Spanning Tree -- 3.2.1 Method 1: Using the Cartesian Product of the Node Cut Sets -- 3.2.2 Method 2: Using the Incidence Matrix -- 3.3 Number of Spanning Trees -- 3.3.1 Matrix Tree Theorem -- 3.4 Enumeration of k-node Connected Path Sets: k-Trees -- Appendix 3A.1: Enumeration of Path Sets Algorithm, Illustration and Matlab® Code Notation -- Appendix 3A.2: Sample program I/O for Figure 3A.1 -- Exercises -- References -- 4 Cut Sets Enumeration -- 4.1 (s, f) Cut Sets Enumeration -- 4.1.1 Method 1: Using Connection Matrix -- 4.1.2 Method 2: Using Minimal Path Sets -- 4.1.2.1 Using Set-theoretic Product of Path Sets -- 4.1.2.2 Using Path Sets Matrix -- 4.1.2.3 Using Path Sets Inversion -- 4.2 Global Cut Sets Enumeration -- 4.2.1 Testing Connectivity of a Specified Node Set -- 4.2.1.1 Node Fusion Technique -- 4.2.2 Generation of Node Set Combination from its Lower Order Node-Sets -- 4.2.3 Checking Validity of a Node Set -- 4.2.4 Formation of Cutset -- 4.2.5 General Algorithm to Enumerate Minimal Cutsets for a Reliability Measure -- Appendix 4A.1: Node Fusion Technique and Generation of Node Set Combination -- Appendix 4A.2: Code for Checking Validity of a Node Set and Converting Node-Sets into Link Cutsets -- Appendix 4A.3: Sample Program I/O for Network Graph of Figure 4.3 -- Appendix 4A.4: g-Terminal Reliability Evaluation Program Sample I/O for Example of Figure 4.3 and Results Provided by the Program (Output of g-reliability Expression for the Figure 4.3 for Method HM-1 of (Chaturvedi &amp -- Misra, 2002) -- Exercises -- References -- 5 Reliability Evaluation using MVI Techniques…”

Tabla de Contenidos: “…. -- Visualizing contributor location frequency on Wikipedia -- How to do it -- Creating a word cloud from a StackOverflow job listing -- Getting ready -- How to do it -- Crawling links on Wikipedia -- Getting ready -- How to do it -- How it works -- Theres more... -- Visualizing page relationships on Wikipedia -- Getting ready -- How to do it -- How it works -- There's more... -- Calculating degrees of separation -- How to do it -- How it works -- There's more... -- Chapter 9: Creating a Simple Data API -- Introduction -- Creating a REST API with Flask-RESTful -- Getting ready -- How to do it -- How it works -- There's more... -- Integrating the REST API with scraping code -- Getting ready -- How to do it -- Adding an API to find the skills for a job listing -- Getting ready -- How to do it -- Storing data in Elasticsearch as the result of a scraping request -- Getting ready -- How to do it -- How it works -- There's more... -- Checking Elasticsearch for a listing before scraping -- How to do it -- There's more…”