Tabla de Contenidos: “…LIFE e Robot -- 16. Big Data, microprocessori, sicurezza ed educazione al valore del dato nella scuola primaria…”

Tabla de Contenidos: “…; PRIVACY LEVEL AGREEMENT; DATA PROTECTION CERTIFICATION; END NOTES; Chapter 8 - Cloud Security Alliance Research; BIG DATA WORKING GROUP; CLOUD DATA GOVERNANCE; CLOUDCERT; CLOUDTRUST PROTOCOL; ENTERPRISE ARCHITECTURE WORKING GROUP; INCIDENT MANAGEMENT AND FORENSICS; INNOVATION INITIATIVE; SECURITY AS A SERVICE; SECURITY GUIDANCE FOR CRITICAL AREAS OF FOCUS; SOFTWARE DEFINED PERIMETER; END NOTES; Chapter 9 - Dark Clouds, What to Do In The Event of a Security Incident…”

Tabla de Contenidos: “…Intelligence Artificielle : des Big-Data au Cerveau -- Chapitre 8. Former les machines à la compréhension du langage naturel -- Chapitre 9. …”

Tabla de Contenidos: “…-- 12 Creating a Successful Cloud Roadmap -- Crossing Your Chasms -- Planning Your Journey -- 13 Conclusion -- Big Data -- Big Architecture -- Communications, Networking, and the Interconnectedness of All Things -- Privacy -- The Rise of the Broker -- The Rise of Community Clouds -- Rapidly Changing Billing Models -- Silver Clouds, Dark Linings -- Endnotes…”

Tabla de Contenidos: “…. -- Chapter 9: Interacting with Big Data -- Introduction -- Obtaining a word count from a big-text data source -- How to do it... -- How it works... -- Obtaining a sorted word count from a big-text source -- How to do it... -- How it works... -- Examining big-text log file access -- How to do it... -- How it works... -- Computing prime numbers using parallel operations -- How to do it... -- How it works... -- Analyzing big-text data -- How to do it... -- How it works... -- Analyzing big data history files -- How to do it... -- How it works... -- Chapter 10: Jupyter Security -- Introduction -- How much risk? …”

Tabla de Contenidos: “…Evaluating relations between variables with ANOVA -- Chapter 4: Dealing with Data and Numerical Issues -- Introduction -- Clipping and filtering outliers -- Winsorizing data -- Measuring central tendency of noisy data -- Normalizing with the Box-Cox transformation -- Transforming data with the power ladder -- Transforming data with logarithms -- Rebinning data -- Applying logit() to transform proportions -- Fitting a robust linear model -- Taking variance into account with weighted least squares -- Using arbitrary precision for optimization -- Using arbitrary precision for linear algebra -- Chapter 5: Web Mining, Databases, and Big Data -- Introduction -- Simulating web browsing -- Scraping the Web -- Dealing with non-ASCII text and HTML entities -- Implementing association tables -- Setting up database migration scripts -- Adding a table column to an existing table -- Adding indices after table creation -- Setting up a test web server -- Implementing a star schema with fact and dimension tables -- Using HDFS -- Setting up Spark -- Clustering data with Spark -- Chapter 6: Signal Processing and Timeseries -- Introduction -- Spectral analysis with periodograms -- Estimating power spectral density with the Welch method -- Analyzing peaks -- Measuring phase synchronization -- Exponential smoothing -- Evaluating smoothing -- Using the Lomb-Scargle periodogram -- Analyzing the frequency spectrum of audio -- Analyzing signals with the discrete cosine transform -- Block bootstrapping time series data -- Moving block bootstrapping time series data -- Applying the discrete wavelet transform -- Chapter 7: Selecting Stocks with Financial Data Analysis -- Introduction -- Computing simple and log returns -- Ranking stocks with the Sharpe ratio and liquidity -- Ranking stocks with the Calmar and Sortino ratios -- Analyzing returns statistics…”

Tabla de Contenidos: “…. -- Fakt oder Fake Teil 1: kritischer Umgang mit Inhalten -- Fakt oder Fake Teil 2: das Leben in der Filterblase -- Strategien für einen Faktencheck -- Big Data: ein Schnitzelbrötchen für den Bürger aus Glas -- Big Data: Facebook und unsere Daten -- Facebook - eine Frage der richtigen Einstellung -- Was Metadaten über uns aussagen -- Mein Passwort kenne nur ich -- Digitale Daten -- Sensible Daten oder öffentlicher Plausch? …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- Foreword -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Spark -- Dimensions of big data -- What makes Hadoop so revolutionary? -- Defining HDFS -- NameNode -- HDFS I/O -- YARN -- Processing the flow of application submission in YARN -- Overview of MapReduce -- Why Apache Spark? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Acknowledgments -- About the Companion Website -- Introduction -- Chapter 1 Statistics and Data Science -- 1.1 Big Data: Predicting Pregnancy -- 1.2 Phantom Protection from Vitamin E -- 1.3 Statistician, Heal Thyself -- 1.4 Identifying Terrorists in Airports -- 1.5 Looking Ahead -- 1.6 Big Data and Statisticians -- 1.6.1 Data Scientists -- Chapter 2 Designing and Carrying Out a Statistical Study -- 2.1 Statistical Science -- 2.2 Big Data -- 2.3 Data Science -- 2.4 Example: Hospital Errors -- 2.5 Experiment -- 2.6 Designing an Experiment -- 2.6.1 A/B Tests -- A Controlled Experiment for the Hospital Plans -- 2.6.2 Randomizing -- 2.6.3 Planning -- 2.6.4 Bias -- 2.6.4.1 Placebo -- 2.6.4.2 Blinding -- 2.6.4.3 Before‐after Pairing -- 2.7 The Data -- 2.7.1 Dataframe Format -- 2.8 Variables and Their Flavors -- 2.8.1 Numeric Variables -- 2.8.2 Categorical Variables -- 2.8.3 Binary Variables -- 2.8.4 Text Data -- 2.8.5 Random Variables -- 2.8.6 Simplified Columnar Format -- 2.9 Python: Data Structures and Operations -- 2.9.1 Primary Data Types -- 2.9.2 Comments -- 2.9.3 Variables -- 2.9.4 Operations on Data -- 2.9.4.1 Converting Data Types -- 2.9.5 Advanced Data Structures -- 2.9.5.1 Classes and Objects -- 2.9.5.2 Data Types and Their Declaration -- 2.10 Are We Sure We Made a Difference? …”

Tabla de Contenidos: “…4.4.1 Mathematical Model -- 4.4.2 Advantages of the Proposed Model -- 4.5 Discussion and Conclusion -- References -- Chapter 5 Integrating IoT and Deep Learning-The Driving Force of Industry 4.0 -- 5.1 Motivation and Background -- 5.2 Bringing Intelligence Into IoT Devices -- 5.3 The Foundation of CR-IoT Network -- 5.3.1 Various AI Technique in CR-IoT Network -- 5.3.2 Artificial Neural Network (ANN) -- 5.3.3 Metaheuristic Technique -- 5.3.4 Rule-Based System -- 5.3.5 Ontology-Based System -- 5.3.6 Probabilistic Models -- 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network -- 5.5 Realization of CR-IoT Network in Daily Life Examples -- 5.6 AI-Enabled Agriculture and Smart Irrigation System-Case Study -- 5.7 Conclusion -- References -- Chapter 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment -- 6.1 Introduction -- 6.2 Overview of Blockchain -- 6.3 Components of Blockchain -- 6.3.1 Data Block -- 6.3.2 Smart Contracts -- 6.3.3 Consensus Algorithms -- 6.4 Safety Issues in Blockchain Technology -- 6.5 Usage of Big Data Framework in Dynamic Supply Chain System -- 6.6 Machine Learning and Big Data -- 6.6.1 Overview of Shallow Models -- 6.6.1.1 Support Vector Machine (SVM) -- 6.6.1.2 Artificial Neural Network (ANN) -- 6.6.1.3 K-Nearest Neighbor (KNN) -- 6.6.1.4 Clustering -- 6.6.1.5 Decision Tree -- 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems -- 6.7.1 Replenishment Planning -- 6.7.2 Optimizing Orders -- 6.7.3 Arranging and Organizing -- 6.7.4 Enhanced Demand Structuring -- 6.7.5 Real-Time Management of the Supply Chain -- 6.7.6 Enhanced Reaction -- 6.7.7 Planning and Growth of Inventories -- 6.8 IoT-Enabled Blockchains -- 6.8.1 Securing IoT Applications by Utilizing Blockchain -- 6.8.2 Blockchain Based on Permission -- 6.8.3 Blockchain Improvements in IoT…”

Tabla de Contenidos: “…-- Best practices for bidirectional cross-filtering -- Understanding what's the right cardinality -- Understanding cardinality -- Why cardinality matters -- Choosing the right cardinality -- Handling large and complex datasets -- Understanding big data -- Challenges of working with big data in Power BI -- Best practices for handling big data -- Avoiding circular references -- Understanding circular references -- Best practices for avoiding circular references -- Summary -- Questions -- Further reading -- Part 4 - Paginated Reports, Automations, and OpenAI -- Chapter 13: Preparing Data for Paginated Reporting -- Technical requirements -- Understanding the importance of paginated reports…”

Tabla de Contenidos: “…: Eating and Blessing with Rav Naḥman -- Marjorie Lehman -- 8 Contemporary Criteria for the Declaration of Death -- Daniel S. Nevins -- 9 Big Data Meets the Shulḥan Arukh -- Michael Pitkowsky -- 10 The Joint Bet Din of the Conservative Movement -- Mayer E. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Google Cloud Platform -- Introduction to cloud computing -- Introducing GCP -- GCP services -- Compute services -- Storage services -- Networking services -- Big data -- Data centers and regions -- Relating AWS and Azure to GCP -- Exploring GCP -- Creating your first project -- Using the command line -- Summary -- Chapter 2: Google Cloud Platform Compute -- Google Compute Engine -- f1-micro bursting machine types -- Mega-memory machine types -- Images -- Creating a VM instance -- Preemptible VM instances -- Live migration -- Instance templates -- Google App Engine -- Kubernetes engine -- Node pools -- Google Cloud Functions -- Summary -- Chapter 3: Google Cloud Platform Storage -- Persistent storage -- Google Cloud Storage buckets -- Google Cloud Spanner -- Google Cloud SQL -- Google Cloud Bigtable -- Summary -- Chapter 4: Google Cloud Platform Networking -- VPC networks -- Routes -- Firewall -- VPC network peering -- Private Google access -- Other networking concepts -- Load balancing -- Google Cloud CDN -- Cloud VPN -- Cloud interconnect -- Summary -- Chapter 5: Google Cloud Platform Containers -- Kubernetes concepts -- Administering a cluster -- Configuring cluster networking -- Multi-zone clusters -- Preemptible instances -- Summary -- Chapter 6: Google Cloud Platform Operations -- Stackdriver monitoring and logging -- Logging -- Stackdriver error reporting -- Stackdriver debugger -- Stackdriver profiler -- Stackdriver Trace -- Summary -- Chapter 7: Google Cloud Platform Identity and Security -- Infrastructure and cloud platform security -- Identity and access management -- Key management service -- Cloud security scanner -- Data loss prevention -- Security keys -- Summary…”

Tabla de Contenidos: “…Lawler IIIm -- 15 The Age of Big Data and Talent Analytics Kevin Oakes and Cliff Stevenson -- Section IV: Growing Talent -- 16 Adapting to Changing Workforce Policy Issues Jeanette K. …”

Tabla de Contenidos: “…Einleitung (Julia Hahmann, Ulrike Knobloch, Melanie Kubandt, AnnaOrlkowski, Christina Plath) -- Geschlecht im Fokus der Soziologie und in den Erziehungswissenschaften -- Soziologische Feminismen: Ein Plädoyer für die Stärkung herrschaftskritischer Perspektiven am Beispiel der Alter(n)ssoziologie (Julia Hahmann) -- Männliche Legitimationsstrategien zur ungleichen vergeschlechtlichten Arbeitsteilung in Familie und Erwerbsarbeit (Jenny Ebert) -- Die gemiedene Kategorie der Psyche in der intersektionalen Diskriminierungskritik - Psychismus als Diskriminierungsform denken wagen (Sonja Lauff) -- Digitalisierungsprozesse in Kindheit und Kindertagesstätten - Ein kritischer Diskurs zu "BigData" in kindlichen Lebenswelten am Beispiel von Geschlecht (Jaqueline Veenker) -- Geschlechterforschung in der Ökonomie -- Plurale Feministische Ökonomie und ihre normativen Grundlagen (Ulrike Knobloch) -- Inputs zur Pluralen Feministischen Ökonomie -- Materialistischer Feminismus heute (Ann-Christin Kleinert) -- Feministisch-ökologische (Postwachstums-)Ökonomie (Corinna Dengler) -- Feministisch-ökologische Ökonomie der Zeit (Hanna Völkle) -- Geschlecht in der experimentellen Verhaltensökonomie (Bernd Josef Leisen) -- Peasant Women's Roles in Agroecology Facing Neo-Extractivism in Latin America (Ana Alvarenga de Castro) -- Herausforderungen interdisziplinärer Geschlechterforschung…”

Tabla de Contenidos: “…Para recordar -- CAPÍTULO 7. BIG DATA -- 1. Naturaleza y esencia -- 2. Historia y e-liderazgo -- 3. …”

“…Una ExO puede transformar el modo lineal e incremental en que las empresas tradicionales crecen, mediante el uso de activos como su comunidad, personal bajo demanda, Big Data, Inteligencia Artificial y otras nuevas tecnologías, hasta alcanzar un rendimiento diez veces superior al de empresas similares.Tres visionarios del mundo de los negocios ? …”
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“…New methods for analysis of big data such as financial markets, automobile traffics, epidemic spreading, world-trades and social media communications are provided to clarify complex interaction and distributions underlying in these social phenomena. …”

Tabla de Contenidos: “…Current healthcare, big data, and machine learning 1</p> <p>Adam Bohr and Kaveh Memarzadeh</p> <p>1.1 Current healthcare practice 1</p> <p>1.2 Value-based treatments and healthcare services 5</p> <p>1.3 Increasing data volumes in healthcare 10</p> <p>1.4 Analytics of healthcare data (machine learning and deep learning) 16</p> <p>1.5 Conclusions/summary 21</p> <p>References 22</p> <p>2. …”

Tabla de Contenidos: “…; IoT reference model; IoT platforms; IoT verticals; Big data and IoT; Infusion of AI -- data science in IoT; Cross-industry standard process for data mining; AI platforms and IoT platforms; Tools used in this book; TensorFlow; Keras; Datasets; The combined cycle power plant dataset; Wine quality dataset; Air quality data; Summary; Chapter 2: Data Access and Distributed Processing for IoT; TXT format Using TXT files in PythonCSV format; Working with CSV files with the csv module; Working with CSV files with the pandas module; Working with CSV files with the NumPy module; XLSX format; Using OpenPyXl for XLSX files; Using pandas with XLSX files; Working with the JSON format; Using JSON files with the JSON module; JSON files with the pandas module; HDF5 format; Using HDF5 with PyTables; Using HDF5 with pandas; Using HDF5 with h5py; SQL data; The SQLite database engine; The MySQL database engine; NoSQL data; HDFS; Using hdfs3 with HDFS; Using PyArrow's filesystem interface for HDFS; Summary; Chapter 3: Machine Learning for IoTML and IoT; Learning paradigms; Prediction using linear regression; Electrical power output prediction using regression; Logistic regression for classification; Cross-entropy loss function; Classifying wine using logistic regressor; Classification using support vector machines; Maximum margin hyperplane; Kernel trick; Classifying wine using SVM; Naive Bayes; Gaussian Naive Bayes for wine quality; Decision trees; Decision trees in scikit; Decision trees in action; Ensemble learning; Voting classifier; Bagging and pasting; Improving your model -- tips and tricksFeature scaling to resolve uneven data scale; Overfitting; Regularization; Cross-validation; No Free Lunch theorem; Hyperparameter tuning and grid search; Summary; Chapter 4: Deep Learning for IoT; Deep learning 101; Deep learning-why now?…”