Tabla de Contenidos: “…Azure Key Vault secrets in ARM templates -- Azure Storage Service Encryption -- Azure Disk Encryption -- Azure SQL Database Security -- Azure Active Directory Managed Service Identity -- Summary -- Questions -- Further reading -- Chapter 11: Governance and Policies -- Technical requirements -- Azure Role-Based Access Control -- Built-in Roles -- Custom Roles -- Creating a Custom Role -- Azure Resource Policies -- Azure AD Privileged Identity Management -- Azure AD Identity Protection -- Azure Security Center -- Advanced Threat Detection -- Azure Endpoint Protection -- Operations Management Suite - Security and Compliance -- Summary -- Questions -- Further reading -- Chapter 12: Artificial Intelligence, IoT, and Media Services -- Technical requirements -- Azure Cognitive Services -- Available services and APIs -- Using the Computer Vision API -- Azure Bot Service -- Creating a Bot from the Azure Portal -- Azure Machine Learning -- Azure IoT Hub -- Azure Event Hub -- Azure IoT Edge -- Azure Stream Analytics -- Azure Time Series Insights -- Azure Media Services -- Azure Media Analytics -- Using the Azure Media Analytics Indexer -- Summary -- Questions -- Further reading -- Chapter 13: Implementing Messaging Solutions -- Technical requirements -- Azure Queue Storage -- Azure Service Bus -- Azure Event Grid -- Routing Events with Azure Event Grid -- Notification Hubs -- Designing an effective messaging architecture -- Summary -- Questions -- Further reading -- Chapter 14: Application Monitoring and Alerting Strategies -- Azure Log Analytics -- Creating a Log Analytics Workspace -- Azure Monitor -- Application Insights -- Azure Service Health -- Azure Advisor -- Address Recommendation from Azure Advisor -- Azure Network Watcher -- Summary -- Questions -- Further reading -- Chapter 15: Exploring Operations Automation Strategies…”

Tabla de Contenidos: “…-- 4.2.1. Toward a holistic vision of engineers -- 4.2.2. Emergence of industry requirements -- 4.3. …”

Tabla de Contenidos: “…-- Impact on Remote Work -- In Times of Stress -- Company Culture and Success -- Retention: The Cost of Replacing Employees -- Controllable Quit Rate -- Shape Your Culture with Remote Work in Mind -- Leaders Set the Tone -- Workplace Transparency -- Be Worthy of Their Trust -- Define Your Vision Well -- Emphasize Meaningful Work -- Stay Engaged -- Get Feedback and Act on It -- Be Clear on Digital Work Policies -- Check Your Headings and Adjust Course as Needed -- Maintaining Culture When Remote -- Be People-Centric and Values-Centric -- Get the Tech You Need -- Ownership and Accountability -- Employee Recognition -- Bonusly -- Nectar -- Fond -- Kudos -- Cooleaf -- Cultural Fit -- Chapter 11 Operations and Administration -- Company Accounts -- Automate -- Paying Bills and Getting Paid -- Melio -- Corpay One -- Bill.com -- Online Bookkeeping and Accounting -- QuickBooks Online -- Wave -- Zoho Books -- FreshBooks -- Human Resources -- No Dedicated HR -- Record Keeping, Compliance, and Benefits -- BambooHR -- Zenefits -- Namely -- Gusto -- Paycor -- Losing People during the Switch -- Remote Hiring Process -- It Begins before the Hire -- All Digital Engagement -- Questions for the Remote Worker -- Remote Work Experience -- Measuring Success -- Ability to Maintain Focus on Work -- Their Workspace…”

Tabla de Contenidos: “…6.5.3 Protocol, Script and Metrics -- 6.5.4 Results -- 6.5.4.1 Appointment test -- 6.5.4.2 Medication test -- 6.6 Conclusions after the User-Centred Process -- 6.7 Characteristics of REMPARK Smartphone Applications -- 6.8 Some Concluding Remarks -- References -- Chapter 7 - Actuator Sub-System: The Auditory Cueing -- 7.1 Introduction -- 7.2 Cueing Strategies for Gait in Parkinson's Disease -- 7.3 State of the Art: Competitive Analysis -- 7.4 REMPARK Auditory Cueing System (ACS) -- 7.5 Outcomes from Field Trials: Future Considerations -- 7.6 A Step Further in REMPARK: Automatic Drug Administration -- 7.7 Conclusion -- References -- Chapter 8 - Disease Management System (DMS) -- 8.1 Introduction -- 8.2 Disease Management System Application -- 8.3 DMS Functional Organization -- 8.4 Advantages Using the Disease Management System (DMS) -- 8.4.1 Advantages for the Clinical Team -- 8.4.2 Advantages for the Patients -- 8.4.3 Advantages for the Organization -- 8.5 Conclusions, Discussion and Vision -- References -- Chapter 9 - REMPARK System Assessment: Main Results -- 9.1 Introduction -- 9.2 Description of Main Communication Flows -- 9.2.1 Type of Transmitted Information -- 9.2.2 Security Aspects -- 9.2.3 Final Deployment and Implementation of the REMPARK Platform -- 9.3 Pilot for the REMPARK System Assessment: Description -- 9.3.1 Definition of the Pilots' Objectives and Eligibility Criteria -- 9.3.2 Design of the Study -- 9.3.3 Pre-Pilot Conclusions -- 9.4 REMPARK Pilots' Execution and Obtained Results -- 9.4.1 Performance of the System -- 9.4.2 Validity of the ON-OFF Detection Algorithm -- 9.4.2.1 The methodology -- 9.4.2.2 Validation of the ON-OFF diaries: available data -- 9.4.2.3 Results of the ON/OFF state detection -- 9.4.3 Non-Motor Symptoms Descriptive Analysis -- 9.4.4 Efficacy and Effectiveness of the Cueing System…”

Tabla de Contenidos: “…Tracking app conversions with Firebase -- AdMob -- Android Studio and initializing the SDK -- Summary -- Chapter 13: Flexible NoSQL and Cloud Firestore -- Cloud Firestore -- Adding and managing data -- Choosing a data structure for your Firestore project -- Querying data -- Securing data -- Offline data support -- Summary -- Chapter 14: Analytics Data, Clairvoyant, Firebase Predictions -- Firebase Predictions -- Setting up Firebase Predictions -- Optimizing monetization -- Optimizing promotions -- Preventing churn -- Predefined predictions -- Predictions and risk tolerance -- Summary -- Chapter 15: Training Your Code and ML Kit -- Firebase ML Kit -- Vision -- Recognizing text -- Setting up ML Kit for recognizing text -- On-device text recognition -- Cloud-Based text recognition -- Face detection -- Setting up ML Kit for face detection -- Barcode scanning -- Setting up ML Kit for barcode scanning -- Custom models -- ML Kit and text recognition -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Chapter 12 Opportunities and Barriers of High-Voltage Direct Current Grids: A State-of-the-Art Analysis -- INTRODUCTION -- PRIORITY CORRIDORS: LINKING LARGE RENEWABLE ENERGY SOURCES (RES) GENERATION WITH CONSUMPTION CENTERS -- THE SUPERGRID VISION -- BARRIERS AND FACILITATORS -- CONCLUSION -- REFERENCES -- Chapter 13 Wireless Power Transmission: Inductive Coupling, Radio Wave, and Resonance Coupling -- INTRODUCTION -- HISTORY OF THE WPT -- INDUCTIVE COUPLING AND RESONANCE COUPLING -- MICROWAVE POWER TRANSMISSION -- CONCLUSIONS -- REFERENCES -- Part III Flexibility Measures -- Chapter 14 The Role of Large-Scale Energy Storage Under High Shares of Renewable Energy -- INTRODUCTION -- GENERAL CHARACTERISTICS OF THE POWER GRID -- POWER QUALITY -- FEATURES OF VARIATIONS DUE TO RENEWABLES -- PROSPECTS OF ENERGY STORAGE GROWTH -- ENERGY STORAGE SYSTEMS -- CONCLUSIONS -- REFERENCES -- FURTHER READING -- Chapter 15 The Role of Electric Vehicles in Smart Grids -- INTRODUCTION -- APPROACHES FOR THE INTEGRATION OF ELECTRIC VEHICLES INTO POWER SYSTEMS -- ELECTRIC VEHICLE OPERATION OBJECTIVES AND ROLES -- STORAGE FOR MARKET PARTICIPATION AND PROFIT MAXIMIZATION -- UPCOMING CHALLENGES FOR THE INTRODUCTION OF LARGE‐SCALE EV ADOPTION AND MANAGEMENT -- CONCLUSION -- REFERENCES -- Chapter 16 Use of Electric Vehicles or Hydrogen in the Danish Transport Sector in 2050? …”

“…The dominance of vision is so strong in sighted people that touch is sometimes considered as a minor perceptual modality. …”

“…Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Implement RAG's traceable outputs, linking each response to its source document to build reliable multimodal conversational agents Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs Balance cost and performance between dynamic retrieval datasets and fine-tuning static data Book Description RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. …”

Tabla de Contenidos: “…-- Analytics on AWS -- Skills Required to Build and Maintain an AWS Analytics Pipeline -- Chapter 2 The Path to Analytics: Setting Up a Data and Analytics Team -- The Data Vision -- Support -- DA Team Roles -- Early Stage Roles -- Team Lead -- Data Architect -- Data Engineer -- Data Analyst -- Maturity Stage Roles -- Data Scientist -- Cloud Engineer -- Business Intelligence (BI) Developer -- Machine Learning Engineer -- Business Analyst -- Niche Roles -- Analytics Flow at a Process Level -- Workflow Methodology -- The DA Team Mantra: "Automate Everything" -- Analytics Models in the Wild: Centralized, Distributed, Center of Excellence -- Centralized -- Distributed -- Center of Excellence -- Summary -- Chapter 3 Working on AWS -- Accessing AWS -- Everything Is a Resource -- S3: An Important Exception -- IAM: Policies, Roles, and Users -- Policies -- Identity-Based Policies -- Resource-Based Policies -- Roles -- Users and User Groups -- Summarizing IAM -- Working with the Web Console -- The AWS Command-Line Interface -- Installing AWS CLI -- Linux Installation -- macOS Installation -- Windows -- Configuring AWS CLI -- A Note on Region -- Setting Individual Parameters -- Using Profiles and Configuration Files -- Final Notes on Configuration -- Using the AWS CLI -- Using Skeletons and File Inputs -- Cleaning Up!…”

Tabla de Contenidos: “…Chapter 15: Large-Scale Application Design -- Technical requirements -- The introduction of large-scale, cross-platform project organizing -- Large-scale, cross-platform project organization in C++ -- A large-scale, cross-platform project in C++ -- Best practices and strategies for managing a large-scale, cross-platform project in C++ -- Horizontal and vertical scaling -- Horizontal scaling -- Vertical scaling -- Scaling C++ applications -- Horizontal scaling in C++ -- Vertical scaling in C++ -- Designing data-intensive applications -- Data structure -- Data processing -- The main function -- Summary -- Questions -- Further reading -- Part 3: C++ in the AI World -- Chapter 16: Understanding and Using C++ in Machine Learning Tasks -- Technical requirements -- Introduction to AI -- Computer vision -- NLP -- Knowledge reasoning -- ML -- Understanding ML -- Designing an algorithm that learns -- Categories of ML -- Applications of ML -- Neural networks -- Clustering -- Regression analysis -- C++ and ML -- Summary -- Questions -- Further reading -- Chapter 17: Using C++ in Data Science -- Technical requirements -- Introduction to data science -- C++ example -- Data capturing and manipulation -- C++ example -- Data cleansing and processing -- C++ example -- Applying machine learning algorithms -- C++ example -- Data visualization -- C++ example -- Summary -- Questions -- Further reading -- Chapter 18: Designing and Implementing a Data Analysis Framework -- Technical requirements -- Using and processing statistical data types -- C++ example -- Working with tabular and rectangular data -- C++ example -- A complete ETL pipeline design strategy -- C++ example -- Summary -- Questions -- Further reading -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…-- The Importance of the New Vision -- How to Reduce the Confusion -- Capitalization in Names and Data -- Normalizing Names -- Good Design Has a Human Touch -- Understanding the Data -- Toward Object Name Normalization -- Intelligent Keys and Column Values -- The Commandments -- Part II: SQL and SQL*Plus -- 5 The Basic Parts of Speech in SQL -- Style -- Creating the NEWSPAPER Table -- Using SQL to Select Data from Tables -- SELECT, FROM, WHERE, and ORDER BY -- Logic and Value -- Another Use for WHERE: Subqueries -- Combining Tables -- Creating a View -- Expanding the View -- 6 Basic SQL*Plus Reports and Commands -- Building a Simple Report -- Other Features -- Checking the SQL*Plus Environment -- Building Blocks -- 7 Getting Text Information and Changing It -- Datatypes -- What Is a String? …”

Tabla de Contenidos: “…Cover -- About the Authors -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Business-Integrated Quality Systems -- Chapter 1: Organizational Structures -- General Theory of Organization Structure -- The Functional/Hierarchical Structure -- Matrix Organizations -- Cross-Functional Organization Structure -- Process- or Product-Based (Horizontal) Organization Structures -- Forms of Organization -- Chapter 2: The Quality Function -- Juran Trilogy -- Related Business Functions -- Safety -- Regulatory Issues -- Product Liability -- Environmental Issues Relating to the Quality Function -- Chapter 3: Approaches to Quality -- Deming's Approach -- Total Quality Control in Japan -- ISO 9000 Series -- Malcolm Baldrige National Quality Award -- Deming Prize -- European Quality Award -- Total Quality Management (TQM) -- Six Sigma -- Chapter 4: Customer-Focused Organizations -- Part II: Integrated Planning -- Chapter 5: Strategic Planning -- Organizational Vision -- Strategy Development -- Strategic Styles -- Possibilities-Based Strategic Decisions -- Strategic Development Using Constraint Theory -- The Systems Approach -- Systems Thinking -- System Optimization versus Process Improvement -- Systems as Chains -- Basic Constraint Management Principles and Concepts -- Definition of a Constraint -- Types of Constraints -- Four Underlying Assumptions -- Goal and Necessary Conditions -- The Five Focusing Steps -- Tools of Constraint Management -- The Logical Thinking Process -- "Drum-Buffer-Rope" Production Scheduling -- Critical Chain -- Constraint Management Measurements -- Dilemma: System or Process? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with the Internet of Things -- Chapter 1: An Introduction to IoT Architecture, Hardware, and Real-Life Applications -- Technical requirements -- What is IoT? -- The vision of the IoT -- The evolution of the IoT -- The four pillars of IoT -- Setting up the development environment of the book -- Setting up your Arduino IDE environment -- Setting up your GitHub repository -- Choosing between IoT hardware -- Data acquisition -- Data processing and storage -- Connectivity -- Power management -- Designing a simple IoT system diagram -- A high-level design flow for a smart lightbulb -- A high-level design flow for AWS -- Defining systems and processes for smart objects -- Defining a problem -- The three-step model for working on IoT systems -- Creating the flow -- Practical exercise - creating a mini weather station -- Hardware -- Working with the BME680 -- ESP32 Wi-Fi auto-configuration -- ESPAsync_WiFiManager installation -- ESP32 programming OTA with a web server and mDNS -- ESP32 reading the BME680 sensor -- Summary -- Further reading -- Chapter 2: Understanding and Designing IoT Networks -- Technical requirements -- Understanding the fundamentals of network design -- Ranges -- Topology -- Understanding the seven-layer architecture of IoT -- Defining the design of an IoT network -- Defining what constitutes a smart object -- The components of a generic IoT network -- Putting it together -- Designing wired and wireless IoT networks -- Wired and wireless networks -- Cellular networks -- Machine-to-machine IoT networks -- Other IoT networks -- Practicing forming your own networks -- Practical - creating a Raspberry Pi Wi-Fi extender -- Hardware setup -- Coding it up -- Summary -- Further reading…”

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 -- Part 1 Deciding to Buy a Business -- Chapter 1 Preparing to Buy a Business -- Figuring Your Financial Fitness -- Assess your financial position and goals -- Measuring your net worth -- Telling good debt from bad debt -- Reducing debt -- Buying insurance -- Planning for the long term -- Shrink your spending -- Build up your cash reserves -- Stabilize income with part time work -- Assessing and Replacing Benefits -- Retirement savings plans and pensions -- Health insurance -- Disability insurance -- Life insurance -- Dental, vision, and other insurance -- Social Security taxes -- Time off -- Understanding Your Time and Financial Commitments to Buy -- Stepping Toward Buying a Business -- Enlisting Help from Top-Notch Professionals -- Start with free/low-cost industry resources -- Finding and interviewing reputable professionals -- Estimating the Cost of Your Search and Business Acquisition -- Calculating a down payment amount -- Tallying search expenses -- Finding funding -- Chapter 2 Is Buying a Business for You? …”

“…For example, the book uncovers connections between near Whitney Extension Problems and the problem of alignment of data in Euclidean space, an area of considerable interest in computer vision. Written by a highly qualified author, Near Extensions and Alignment of Data in Rn includes information on: Areas of mathematics and statistics, such as harmonic analysis, functional analysis, and approximation theory, that have driven significant advances in the field Development of algorithms to enable the processing and analysis of huge amounts of data and data sets Why and how the mathematical underpinning of many current data science tools needs to be better developed to be useful New insights, potential tools, and mathematical techniques to solve problems in Whitney extensions, signal processing, shortest paths, clustering, computer vision, optimal transport, manifold learning, minimal energy, and equidistribution Providing comprehensive coverage of several subjects, Near Extensions and Alignment of Data in Rn is an essential resource for mathematicians, applied mathematicians, and engineers working on problems related to data science, signal processing, computer vision, manifold learning, and optimal transport…”

Tabla de Contenidos: “…Generic descriptors of vision: Marchesotti et al. -- 6.2. Help in composing beautiful photos -- 6.2.1. …”

Tabla de Contenidos: “…The Job to Be Done -- Developing an Enterprise Mindset -- Enterprise Leaders Create an Enterprise Vision -- Enterprise Leaders Enable Sustained Profitability -- Enterprise Leaders Maintain Execution Excellence -- Enterprise Leaders Build Social Architecture -- Enterprise Leaders Lead Globally -- The Transition to the Role -- Work Values -- Time Application -- Skills -- Typical Transition Issues -- Failing to Connect with How the Enterprise Operates -- Focusing on External Activities -- Avoiding the Soft Side of the Enterprise -- Failing to Sufficiently Answer Board Members' Questions -- Refusing to Consider Other Business Models -- Leading the Business Instead of Leading the Business Leaders -- Part III Application -- 8 Strategies for Implementing the Leadership Pipeline Model -- Designing Leadership Portraits -- Choosing the Implementation Degree -- Situating the Leadership Pipeline Framework -- How Does the Framework Fit with Our Corporate Title Structure? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Forewords -- Product Quotes -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding the Basics of Windows 365 -- Chapter 1: Introduction to Windows 365 -- The Windows 365 vision - moving from buying computers to buying computing -- What is Windows 365 and what is a Cloud PC? …”

Tabla de Contenidos: “…Answers to the practice questions -- Further reading -- Chapter 5: Amazon Data Analytics Services -- Understanding the AWS big data pipeline -- AWS Glue -- Amazon Athena -- The Amazon Kinesis family -- Amazon QuickSight -- Amazon EMR -- MapReduce and Hadoop -- AWS EMR -- Summary -- Practice questions -- Answers to the practice questions -- Further reading -- Chapter 6: Amazon Machine Learning Services -- ML basics and ML pipelines -- ML problem framing -- Data collection and preparation -- Feature engineering -- ML model development -- ML model deployment, testing, and monitoring -- Amazon SageMaker -- DL basics -- Amazon computer vision solutions -- Amazon's NLP solutions -- Amazon Comprehend -- Amazon Transcribe -- Amazon Polly -- Amazon Translate -- Amazon Lex -- Summary -- Practice questions -- Answers to the practice questions -- Further reading -- Chapter 7: Amazon Cloud Security Services -- Amazon cloud security model -- Amazon IAM -- IAM policies -- AWS infrastructure security -- AWS Organizations -- AWS resource security -- Amazon data encryption -- AWS logging, monitoring, and incident handling -- Case study - an AWS threat detection and incident handling ecosystem -- Automatic threat detection -- Automatic notification -- Automatic remediation -- Summary -- Practice questions -- Answers to the practice questions -- Further reading -- Part 2: Comprehending GCP Cloud Services -- Chapter 8: Google Cloud Foundation Services -- Google Cloud resource hierarchy -- Google Cloud compute -- Google Compute Engine -- Google Kubernetes Engine -- Google Cloud Storage -- Google Cloud networking -- Summary -- Practice questions -- Further reading -- Chapter 9: Google Cloud's Database and Big Data Services -- Google Cloud database services -- Google Cloud SQL -- Google Cloud Spanner -- Google Cloud Firestore -- Google Cloud Bigtable…”

Tabla de Contenidos: “…Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection…”