Computer und IT
Deep Reinforcement Learning in Unity
Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity.This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book.DEEP REINFORCEMENT LEARNING IN UNITY provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks.Deep learning in games makes the agents learn how they can perform better and collect their rewards in adverse environments without user interference. The book provides a thorough overview of integrating ML Agents with Unity for deep reinforcement learning.WHAT YOU WILL LEARN* Understand how deep reinforcement learning works in games* Grasp the fundamentals of deep reinforcement learning * Integrate these fundamentals with the Unity ML Toolkit SDK* Gain insights into practical neural networks for training Agent Brain in the context of Unity ML Agents* Create different models and perform hyper-parameter tuning* Understand the Brain-Academy architecture in Unity ML Agents* Understand the Python-C# API interface during real-time training of neural networks* Grasp the fundamentals of generic neural networks and their variants using Tensorflow* Create simulations and visualize agents playing games in UnityWHO THIS BOOK IS FORReaders with preliminary programming and game development experience in Unity, and those with experience in Python and a general idea of machine learningABHILASH MAJUMDER is a natural language processing research engineer for HSBC (UK/India) and technical mentor for Udactiy (ML). He also has been associated with Unity Technologies and was a speaker at Unite India-18, and has educated close to 1,000 students from EMEA and SEPAC (India) on Unity. He is an ML contributor and curator for Open Source Google Research and Tensorflow, and creator of ML libraries under Python Package Index (Pypi). He is an online educationalist for Udemy and a deep learning mentor for Upgrad.Abhilash was an apprentice/student ambassador for Unity Technologies where he educated corporate employees and students on using general Unity for game development. He was a technical mentor (AI programming) for the Unity Ambassadors Community and Content Production. He has been associated with Unity Technologies for general education, with an emphasis on graphics and machine learning. He is one of the first content creators for Unity Technologies India since 2017.Chapter 1: Introduction to Reinforcement LearningSub -Topics1. Markov Models and State Based Learning2. Bellman Equations3. Creating a Multi Armed Bandit RL simulation.4. Value and Policy iteration.Chapter 2: Pathfinding and NavigationSub - Topics1. Pathfinding in Unity2. Navigation Meshes3. Creating Enemy AIChapter 3: Setting Up ML Agents Toolkit SDKSub - Topics:1. Installing ML Agents2. Configuring Brain Academy3. Linking ML Agents with Tensorflow with Jupyter Notebooks4. Playing with ML agents samplesChapter 4: Understanding Brain Agents and AcademySub - Topics:1. Understanding the architecture of Brain2. Training different Agents with Single Brain3. Generic HyperparametersChapter 5: Deep Reinforcement LearningSub - Topics:1. Fundamentals of Mathematical Machine Learning with Python2. Deep Learning with Keras and Tensorflow3. Deep Reinforcement Learning Algorithms4. Writing neural network for Deep Q learning for Brain5. Hyperparameter Tuning for Optimization6. Memory-based LSTM Network Design with Keras for Brain7. Building an AI Agent for Kart Game Using Trained NetworkChapter 6: Competitive Networks for AI AgentsSub - Topics:1. Cooperative Network and Adversarial Network2. Extended Reinforcement Learning–Deep Policy Gradients3. Simulations Made with Unity ML Agents4. Simulating AI Autonomous Agent for Self-drivingChapter 7: Case Study – Obstacle Tower ChallengeSub - Topics:1. Obstacle Tower Challenge2. Unity ML Agents Challenge3. Research Developments from Unity AI4. Playing with the Open AI Gym Wrapper
Introducing Microsoft Access Using Macro Programming Techniques
Learn Microsoft Access by building a powerful database application from start to finish.Microsoft Access ships with every version of Office, from Office 2019 to Office 365 Home and Personal editions. Most people understand the value of having a reliable contact database, but few realize that Access can be an incredibly valuable data tool and an excellent gateway for learning database development.INTRODUCING MICROSOFT ACCESS USING MACRO PROGRAMMING TECHNIQUES approaches database development from a practical and experiential standpoint. You will learn important data concepts as you journey through each step of creating a database using Access. The example you will build takes advantage of a massive amount of data from an external source of nutritional data (USDA). You will leverage this freely available repository of information in multiple ways, putting Access to the test in creating powerful business solutions that you can then apply to your own data sets. The tables and records in this database will be used to demonstrate key relational principles in Access, including how to use the relationship window to understand the relationships between tables and how to create different objects such as queries, forms, reports, and macros. Using this approach, you will learn how desktop database development can be a powerful solution to meet your business needs.WHAT YOU WILL LEARN* Discover the relational database and how it is different from other databases* Create database tables and establish relationships between them to create a solid relational database system* Understand the concept and importance of referential integrity (RI) in data and databases* Use different types of Access queries to extract the information you need from the database* Show database information in individual, customized windows using Access Forms* Present insightful information about the database using Access Reports* Automate your database solutions with macrosWHO THIS BOOK IS FORAnyone who wants to learn how to build a database using Microsoft Access to create customized solutions. It is also useful for those working in IT managing large contact data sets (healthcare, retail, etc.) who need to learn the basics in order to create a professional database solution. Readers should have access to some version of Microsoft Access in order to perform the exercises in this book.FLAVIO MORGADO is a food engineer with an MSc. degree in food science and technology, a VBA professional developer, and a professor of epidemiology, statistics, and medical informatics at UNIFESO, a health sciences university in Rio de Janeiro, Brazil. Flavio has written more than 30 books, including Programming Excel with VBA (Apress) and Microsoft Word Secrets (Apress), and has translated many technical books. He also loves animals and the exquisite nature of the surrounding rainforest, and when he is not teaching, writing, or developing, he can be found running or riding his mountain bike through the Teresopolis Mountains, followed by his eight dogs, or spending time on the stunningly beautiful beaches of Cabo Frio in Rio de Janeiro.Chapter 1: A Primer on DatabasesChapter 2: Creating and Using Database TablesChapter 3: Creating QueriesChapter 4: Using FormsChapter 5: Using MacrosChapter 6: Using Reports
Beginning T-SQL
Get a performance-oriented introduction to the T-SQL language underlying the Microsoft SQL Server and Azure SQL database engines. This fourth edition is updated to include SQL Notebooks as well as up-to-date syntax and features for T-SQL on-premises and in the Azure cloud. Exercises and examples now include the WideWorldImporters database, the newest sample database from Microsoft for SQL Server. Also new in this edition is coverage of JSON from T-SQL, news about performance enhancements called Intelligent Query Processing, and an appendix on running SQL Server in a container on macOS or Linux.BEGINNING T-SQL starts you on the path to mastering T-SQL with an emphasis on best practices. Using the sound coding techniques taught in this book will lead to excellent performance in the queries that you write in your daily work. Important techniques such as windowing functions are covered to help you write fast-executing queries that solve real business problems.The book begins with an introduction to databases, normalization, and to setting up your learning environment. You will learn about the tools you need to use such as SQL Server Management Studio, Azure Data Studio, and SQL Notebooks. Each subsequent chapter teaches an aspect of T-SQL, building on the skills learned in previous chapters. Exercises in most chapters provide an opportunity for the hands-on practice that leads to true learning and distinguishes the competent professional.A stand-out feature in this book is that most chapters end with a Thinking About Performance section. These sections cover aspects of query performance relative to the content just presented, including the new Intelligent Query Processing features that make queries faster without changing code. They will help you avoid beginner mistakes by knowing about and thinking about performance from day 1.WHAT YOU WILL LEARN* Install a sandboxed SQL Server instance for learning* Understand how relational databases are designed* Create objects such as tables and stored procedures* Query a SQL Server table * Filter and order the results of a query* Query and work with specialized data types such as XML and JSON* Apply modern features such as window functions* Choose correct techniques so that your queries perform wellWHO THIS BOOK IS FORAnyone who wants to learn T-SQL from the beginning or improve their T-SQL skills; those who need T-SQL as an additional skill; and those who write queries such as application developers, database administrators, business intelligence developers, and data scientists. The book is also helpful for anyone who must retrieve data from a SQL Server database.KATHI KELLENBERGER is an editor and DevOps advocate at Redgate Software and a Microsoft Data Platform MVP. She has been working with SQL Server for over 20 years, starting with version 6.5. She has worked as a developer, database administrator, and consultant. Kathi has been involved with almost 20 book projects as an author, co-author, or technical editor, and enjoys presenting at events, webinars, and user groups. When not working, she teaches T-SQL to beginners at a non-profit in St. Louis, Missouri that helps people transition to tech careers.LEE EVEREST is a SQL Server developer who has worked with the product since version 6.5. He has taught part-time at Dallas College North Lake Campus for 18 years, and has had several students move on to companies such as Microsoft, filling roles from support engineer to data scientist to vice president. When Lee isn’t working, he enjoys computers, golf, and fishing.1. Getting Started2. Exploring Database Concepts3. Writing Simple SELECT Queries4. Using Built-In Functions and Expressions5. Joining Tables6. Building on Subqueries, Common Table Expressions, and Unions7. Grouping and Summarizing Data8. Discovering Windowing Functions9. Advanced WHERE Clauses10. Manipulating Data11. Managing Transactions12. Understanding T-SQL Programming Logic13. Implementing Logic in the Database14. Expanding on Data Type Concepts15. Working with XML and JSON16. Writing Advanced Queries17. Where to Go Next?Appendix A: SQL Server for Linux and macOSAppendix B: Using SSMSAppendix C: SQL Notebooks
Manipulationssichere Cloud-Infrastrukturen
Im Rahmen der sich beschleunigenden Digitalisierung wandern sowohl in der Privatwirtschaft als auch den öffentlichen Verwaltungen viele als kritisch bewertete Anwendungen und Dienste in die Cloud. Big Data, Maschinelles Lernen und Künstliche Intelligenz bieten viele Vorteile, werfen aber wachsende Sicherheits- und Datenschutzprobleme auf. Die Sicherheit der informationstechnischen Systeme, einschließlich und insbesondere der Cloud, wird somit zum Dreh- und Angelpunkt einer zuverlässigen, nachhaltigen und sicheren Wirtschaft und Verwaltung.Das vorliegende Buch gibt Antworten auf die von Sicherheitsverantwortlichen und -forschern gleichermaßen gestellte Frage „Wieviel Sicherheit ist genug?“. Dabei werden der rechtliche Rahmen beleuchtet, das Vertrauensdilemma des Cloud Computing herausgearbeitet und die grundsätzlich zur Verfügung stehenden Optionen für Cloud-Sicherheit mit einer Modellierung der Erfolgswahrscheinlichkeit der Angreifer beschrieben und quantitativ analysiert. Es wird gezeigt, wie mit dem Konzept einer manipulationssicheren, versiegelten Verarbeitung der notwendige Durchbruch bezüglich Datenschutz und IT-Sicherheit erzielt werden kann.Mit verschiedenen praktischen Anwendungsfällen der Sealed-Cloud-Technologie wird gezeigt, wie mit solch europäisch implementiertem „Confidential Cloud Computing“ Souveränität in der Datenökonomie gewonnen werden kann.DR. HUBERT A. JÄGER ist Unternehmer und Experte für Innovationen im Bereich der nachhaltigen Digitalisierung. Er baute zusammen mit Dr. Rieken die Uniscon GmbH auf und hält zahlreiche Patente zu unterschiedlichen Themen in der Informationstechnik und Telekommunikation, insbesondere zu Cloud-Sicherheit.DR. RALF O. G. RIEKEN ist Gründer und COO der Uniscon GmbH, einem Anbieter von hochsicheren Cloud-Lösungen. Er hatte zuvor verantwortliche Positionen bei führenden IT- und Netzinfrastrukturlieferanten inne, u.a. im Silicon Valley. Stand der Technik zum Cloud-Computing - Anforderungen geschäftskritischer Anwendungen - Gefühlte und reale Risiken - Überblick zu Konzepten für IT-Sicherheit und Datenschutz in der Cloud - Sealed Processing – ein rein technische Ansatz für Security - Praktische Anwendungen
Digitales Shopfloor Management in SAP-Systemumgebungen
Dieser Ratgeber zeigt Lösungsvarianten sowie Vorgehensmodelle auf und liefert einen schnellen Überblick, Entscheidungshilfen, praxisgerechte Hinweise, Erfahrungshintergrund aus Projekten und Expertenwissen für Ihre Digitalisierungsprojekte in der Fertigung. Erstellen Sie auf Grundlage dieses Buchs eine Roadmap mit den richtigen Prioritäten zur erfolgreichen Digitalisierung Ihrer Produktionsprozesse und verschaffen Sie sich damit Wettbewerbsvorteile in Ihrer Branche.DIPL.-INFORM. MANFRED DIETRICH hat über 20 Jahre Erfahrung in der Beratung von Fertigungsunternehmen und Optimierung von Produktionsabläufen. Mit der deTask CS GmbH (www.detask.de) begleitet er Digitalisierungsvorhaben in der Produktion in unterschiedlichen Branchen der Fertigungsindustrie.Ist-Analyse.-Lösungsbausteine und Varianten.-BI, KI und Cloud.-Erfolgsfaktoren.-Roadmap.
Microsoft Azure Architect Technologies and Design Complete Study Guide
BECOME A PROFICIENT MICROSOFT AZURE SOLUTIONS ARCHITECTAzure certifications are critical to the millions of IT professionals Microsoft has certified as MCSE and MCSA in Windows Server in the last 20 years. All of these professionals need to certify in key Azure exams to stay current and advance in their careers. Exams AZ-303 and AZ-304 are the key solutions architect exams that experienced Windows professionals will find most useful at the intermediate and advanced points of their careers.Microsoft Azure Architect Technologies and Design Complete Study Guide Exams AZ-303 and AZ-304 covers the two critical Microsoft Azure exams that intermediate and advanced Microsoft IT professionals will need to show proficiency as their organizations move to the Azure cloud.* Understand Azure* Set up your Microsoft Cloud network* Solve real-world problems* Get the confidence to pass the examBy learning all of these things plus using the Study Guide review questions and practice exams, the reader will be ready to take the exam and perform the job with confidence.BENJAMIN PERKINS is a Senior Support Escalation Engineer at Microsoft. He’s an expert C# programmer with a focus on modern cloud and web technologies. He has 15+ years of experience in designing, developing, deploying, administering, and managing enterprise IT solutions.WILLIAM PANEK, MCP, MCSE, MCSA, MCTS, MCITP, CCNA, is a Five-Time Microsoft MVP Winner. He has taught at Boston University, Clark University, and the University of Maryland, and presently conducts live online classes for StormWind Studios www.stormwind.com Introduction xxiAssessment Test xxxixCHAPTER 1 GAINING THE AZURE SOLUTIONS ARCHITECT EXPERT CERTIFICATION 1The Journey to Certification 3A Strategy to Pass the Azure Exams 5Use Azure Daily 5Read Azure Articles, Keeping Yourself Current 6Recognize Azure Product Names, Features, and Functionalities 9Strive for a Deep Knowledge of a Few, Some Knowledge of Many, and a Basic Knowledge of All 10An Introduction to “Must-Know” Azure Features 12Azure Active Directory and Security 12Networking 13Azure Virtual Machines 15Azure App Service 16Azure Functions 18API Management 19Azure Monitor 20Azure SQL 22Azure Cosmos DB 24Azure Storage 25Service Bus 28Site Recovery 30Azure Bastion 32Summary 32Exam Essentials 33Key Terms 34Review Questions 35CHAPTER 2 SECURITY AND IDENTITY 39Azure Active Directory 40Add a Custom Domain to Azure Active Directory 44AAD Connect 49Connect Health 51Directory Objects 52Single Sign-On 52B2B Collaboration 53Self-Service Password 54Application Proxy 54Service Level Agreement 56Identity Protection 57Conditional Access 59Multifactor Authentication 66Privileged Identity Management 74Managed Identities 75Azure AD Domain Services 76Role-Based Access Control 78How to Control Who or What Has Access 84How to Provide Permissions to Resources 85How Are the Permissions to Resources Controlled? 87Custom Roles 87Hardware and Network Security 92Microsoft Trust Center 93Security Center 93Azure Network Security 98Application Gateway/WAF 98Azure DDoS Protection 99Azure Confidential Computing 99Azure Security Products and Techniques 102Shared Access Signature 102Azure Key Vault 103Easy Auth 105Summary 106Exam Essentials 106Review Questions 108CHAPTER 3 NETWORKING 111Microsoft’s Global Network 112Overview of Hybrid Networks 114Azure Virtual Network 115Azure Virtual Networking 117Regions 117Key Features and Capabilities 127Network Security 168Traffic Filtering with NSG, ASG, and NVA 169Application Gateway/WAF 174IP Restrictions 180Network Map and Topology 183Using Azure DNS 184Azure-Provided DNS 187Hybrid Azure Networking 190ExpressRoute 190Site-to-Site VPN Gateway 192Additional Azure Networking Products 201Application Gateway 202Hosting Multiple Websites 206Azure Load Balancer 211Azure Front Door 214Azure Content Delivery Network 215Traffic Manager 217Azure Relay/Hybrid Connection Manager 218Key Terms 220Summary 221Exam Essentials 222Review Questions 224CHAPTER 4 COMPUTE 227An Overview of Compute (Hosting Model) 229Cloud Service Models 229How to Choose the Right Hosting Model 231Architectural Styles, Principles, and Patterns 234Azure Compute Best Practices 237Azure Container Instances 239OS Virtualization, Containers, and Images 241Container Groups and Multicontainers 243Azure Virtual Machines 256Creating Azure Virtual Machines 259Managing Azure Virtual Machines 271Azure App Services 298Web Apps 301Web App for Containers (Linux) 306App Service Environments 308Azure WebJobs 309Azure Batch and HPC 312Storage 316Marketplace 316Azure Functions 317Hosting Plans 319Triggers and Bindings 320Runtime Versions 326Supported Programming Languages 326Service Fabric 328Clusters and Nodes 330Architecture 331Best-Practice Scenarios 332Azure Integration 335Azure Kubernetes Service 336Kubernetes vs. AKS 336Clusters, Nodes, and Pods 338Development and Deployment 338Maintaining and Scaling 342Cloud Services 344Windows Virtual Desktop 345Summary 346Key Terms 347Exam Essentials 348Review Questions 350CHAPTER 5 DATA AND STORAGE 355RDBMS, OLTP, OLAP, and ETL 357Big Data/NoSQL 358Choosing the Right Data Storage Solution 359Document Databases 360Key/Value Pairs 361Graph Databases 362Object Storage 363Relational Database Management System 363Search Engine Databases 365Data Analytics/Data Warehouse 365Shared Files 373Azure Data Store 377Azure SQL Database 382Other Azure Data Stores 420Azure Storage 424Zone Replication 434Data Backup, Migration, and Retention 437Securing Azure Data 443Summary 451Exam Essentials 451Key Terms 452Review Questions 454CHAPTER 6 HYBRID, COMPLIANCE, AND MESSAGING 457Hybrid Solutions 458Hybrid Security 459Hybrid Networking 460Hybrid Computing 462Hybrid Data Solutions 463Azure Cloud Compliance Techniques 463Compliance and Governance 464Security 472Resiliency and Reliability 474Privacy 475Security Center 478Microsoft Cloud App Security 483Azure Messaging Services 484Event vs. Messaging 485How to Choose the Right Messaging Service 485Messaging Patterns 487Event Hubs 492Service Bus 496Azure Storage Queue 498Event Grid 499Logic Apps 503Notification Hubs 505Summary 505Exam Essentials 506Key Terms 506Review Questions 508CHAPTER 7 DEVELOPING FOR THE CLOUD 511Architectural Styles, Principles, and Patterns 512Architectural Styles 513Design Principles 515Cloud Design Patterns 517An Introduction to Coding for the Cloud 523Triggering a Background Job 523Connecting to Regional/Global Database Instances 524Working with the Azure Queue Storage SDK 524Forms, Certificate, Windows, MFA, OpenStandard, Managed Identities, and ServicePrinciple Authentication 525Reading Encrypted Data from a Database 531IDEs and Source Code Repositories 533Implementing Security 534Summary 538Exam Essentials 539Key Terms 539Review Questions 540CHAPTER 8 MIGRATE AND DEPLOY 543Migrating to Azure 544Azure Site Recovery 548Azure Migrate 549Migrating Azure Virtual Machines 551Database Migration 558Migrating Azure App Services 565Import/Export 566Moving Resources in Azure 567Moving Azure App Services 568Moving Azure Virtual Machines 569Deploying Application Code and Azure Resources 576Deploying with Visual Studio 578Deploying with ARM Templates 580Working with DevOps 594Learning Azure Automation 600Process Automation 604Configuration Management 605Update Management 606Other Automation Resources 607Summary 608Exam Essentials 608Key Terms 609Review Questions 610CHAPTER 9 MONITOR AND RECOVER 613Monitoring Azure Resources 615Azure Service Health 619Azure Monitor 621Azure Monitoring by Component 634Additional Monitoring Topics 648Recover Azure Resources 649What is BCDR? 650Azure Recovery Services 651Azure Recovery by Product Type 668Summary 677Exam Essentials 678Review Questions 679APPENDIX ANSWERS TO REVIEW QUESTIONS 681Chapter 1: Gaining the Azure Solutions Architect Certification 682Chapter 2: Security and Identity 683Chapter 3: Networking 684Chapter 4: Compute 685Chapter 5: Data and Storage 687Chapter 6: Hybrid, Compliance, and Messaging 688Chapter 7: Developing for the Cloud 688Chapter 8: Migrate and Deploy 689Chapter 9: Monitor and Recover 690Index 693
Data Science Solutions on Azure
Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads.The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You'll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you'll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning.Data Science Solutions on Azure will reveal how the different Azure services work together using real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem.WHAT YOU'LL LEARN* Understand big data analytics with Spark in Azure Databricks * Integrate with Azure services like Azure Machine Learning and Azure Synaps* Deploy, publish and monitor your data science workloads with MLOps * Review data abstraction, model management and versioning with GitHubWHO THIS BOOK IS FORData Scientists looking to deploy end-to-end solutions on Azure with latest tools and techniques.JULIAN SOH is a cloud solutions architect with Microsoft, focusing in the areas of artificial intelligence, cognitive services, and advanced analytics. Prior to his current role, Julian worked extensively in major public cloud initiatives, such as SaaS (Microsoft Office 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations.PRIYANSHI SINGH is a data scientist by training and a data enthusiast by nature specializing in machine learning techniques applied to predictive analytics, computer vision and natural language processing. She holds a master’s degree in Data Science from New York University and is currently a Cloud Solution Architect at Microsoft helping the public sector to transform citizen services with Artificial Intelligence. She also leads a meetup community based out of New York to help educate public sector employees via hands on labs and discussions. Apart from her passion for learning new technologies and innovating with AI, she is a sports enthusiast, a great badminton player and enjoys playing Billiards. Find her on LinkedIn at https://www.linkedin.com/in/priyanshi-singh5/PART I - INTRODUCTION TO DATA SCIENCE AND ITS RISE TO PROMINENCEChapter 1 Data Science in the modern enterpriseWhat is Data ScienceThe Data Scientists' tools and lingoEthics and ethical AISignificance of Data Science in organizationsCase Studies of applied Data ScienceChapter 2 Most important Statistical Tehniques in Data ScienceTop Statistical Tehniques Data Scientists need to knowSupervised LearningUnsupervised LearningRegression/Classification/ ForecastingBayesian methodTime series analysisLinear regressionSampling methodsReinforcement LearningPART 2 - MACHINE LEARNING IN MICROSOFT AZUREChapter 3 Basics of data preparation and data engineeringIngesting disparate data sourcesPreparing data for analysisData ExplorationFeature EngineeringChapter 4 Introducing Azure Machine LearningAzureML- DataStores/ DatasetsAzure ML Compute/CLusters/inference/batch-realtimeAzure ML Service- Training and BuildingAzure ML Service- DeployingAzure ML Service- PipelinesAzure ML Studio/DesignerAzure Automated Machine Learning (AutoML)Hyperparameter TrainingAzure ML- SecurityCase StudyPART 3 - AZURE DATABRICKSChapter 5 Spark and Big DataSpark and HadoopWhat is Big Data?Why Spark is the platform of choice for Big DataChallenges with Big DataChapter 6 Azure Databricks BasicsWhat is Azure DatabricksAzure Databricks from the Data Engineers' perspectiveAzure Databricks from the Data Scientists' perspectiveChapter 7 Azure DatabricksDeploying the Azure Databricks workspaceCreating and Managing ClustersCreating and managing users and groupsManaging Databricks NotebooksUsing Databricks NotebooksDBFSConnecting to ADLSSample Notebook(s)PART 4 - OPERATIONALIZING DATA SCIENCEChapter 8 Machine Learning OperationsOperationalization concepts and DevOpsMLOps in AzureMLFlow in Azure DatabricksGitChapter 9 Practical MLIntroducing use cases in the different industriesDemocratizing AI through ML
Building Solutions with Microsoft Teams
Explore Microsoft Teams and use its principal tools such as Node.js, npm, Yeoman, Gulp, TypeScript, and React to help you develop for Teams better. This book covers the core components and use cases for Teams apps and guides you through ideas for automation, provisioning, and implementation.Building Solutions with Microsoft Teams starts with an overview of the Microsoft Teams developer platform followed by how to set up your environment for building apps and solutions with Teams. You will then go through various features of conversational bots and learn how to create a bot. You will gain an understanding of the messaging extension and command actions along with tabs for personal, groups, and teams contexts. Moving forward, you will work with SharePoint and Teams together via SharePoint Framework. Finally, you will manage the Teams life cycle and see design guidelines supported by various case studies.After reading this book, you will be able to integrate solutions from Power Apps, Power Automate, Power BI, and Power Virtual agents by using accelerators. You will also be able to leverage your existing skills from SharePoint Framework development.WHAT YOU WILL LEARN* Extend the Teams developer platform capabilities* Understand Microsoft Graph, including lifecycle management, collaboration, calling, and online meetings* Create an app package for your Microsoft Teams app* Connect web services to Microsoft Teams with webhooksWHO THIS BOOK IS FORMicrosoft Teams developers.Jenkins is an Office Development MVP who has been working on SharePoint for more than 16 years, focusing on building custom solutions for Microsoft Teams, SharePoint Framework, Power Platform, Office 365, and SharePoint.He is passionate about SharePoint and actively blogs, organizes events, and speaks at events and international conferences, most recently on the topics of Microsoft Teams, SharePoint Framework (SPFx), Power Apps, Power Automate, and Power Virtual Agents.CHAPTER 1: OVERVIEW: MICROSOFT TEAMS DEVELOPER PLATFORMCHAPTER GOAL: Introduction to Microsoft Teams Developer PlatformNO OF PAGES: 20SUB -TOPICS1. Objectives2. Microsoft Teams Overview3. Key Features of Microsoft Teams4. Teams Architecture5. Teams extensible platform capabilities6. Why do you have to build apps for Teams7. ConclusionCHAPTER 2: BUILDING APPS AND SOLUTIONS WITH MICROSOFT TEAMSCHAPTER GOAL: This chapter covers - setup environment and explains how to create apps for Microsoft Teams using C# on .NET, Nodejs, and Yeoman Generator. Also describes about various contexts and scenarios about teams’ apps line of business.NO OF PAGES: 35SUB - TOPICS:1. Microsoft Teams Developer Platform Overview2. Setup the Development environment for Microsoft Teams3. Register the bot with Microsoft Azure's Bot Framework4. Teams apps in various contexts5. Create an app using yeoman generator6. Use App Studio to create your app7. Create an app package for your Microsoft Teams app8. Create an app using Nodejs9. Create an app using C#10. How to use Adaptive cards in Teams app11. ConclusionCHAPTER 3: INTERACTIVE CONVERSATIONAL BOTSCHAPTER GOAL: This chapter covers various features of conversational bots and explains how to create bot with examples to guide the usersNO OF PAGES: 35SUB - TOPICS1. Overview of bots2. Bots in Microsoft Teams channels and group chats3. Proactive messages from bots4. Exercise 1 - Creating conversational bots for Microsoft Teams5. Exercise 2 - Bots in Microsoft Teams channels and group chats6. Exercise 3 - Proactive messages from bots7. ConclusionCHAPTER 4: MESSAGING EXTENSION AND ACTION COMMENTSCHAPTER GOAL: This chapter covers various features of Messaging extension and explains how to create Messaging extension and command actions with examples to guide the usersNO OF PAGES: 30SUB - TOPICS:1. Overview of message extension2. Microsoft Teams messaging extensions and action commands3. Search command message extensions4. Link unfurling message extensions5. Exercise 1 - Create action command messaging extensions6. Exercise 2 - Create search command messaging extensions7. Exercise 3 - Implement link unfurling messaging extensions8. ConclusionCHAPTER 5: EMBEDDED WEB EXPERIENCES WITH TABSCHAPTER GOAL: This chapter covers various features of tabs and explains how to create tabs for personal, groups and teams contexts with examples to guide the usersNO OF PAGES: 30SUB - TOPICS:1. Overview of Tabs2. Create a custom Microsoft Teams personal tab3. Create a custom Microsoft Teams channel or group tab4. Implement authentication in a custom tab5. Exercise 1 - Create a custom Microsoft Teams personal tab6. Exercise 2 - Create a custom Microsoft Teams channel or group tab7. Exercise 3 - Implement authentication in a custom tab8. ConclusionCHAPTER 6: COLLECT INPUT FROM USER USING TASK MODULESCHAPTER GOAL: This chapter covers various features of task modules and collects input from users and process the inputs with examples to guide the usersNO OF PAGES: 30SUB - TOPICS:1. Overview of Task Modules2. Collecting user input with task modules3. Deep links in task modules using adaptive cards4. Bots using task modules5. Exercise - Collecting user input with task modules6. Exercise 1 - Using adaptive cards and deep links in task7. Exercise 2 - Using task modules with bots8. ConclusionCHAPTER 7: CONNECT WEB SERVICES TO MICROSOFT TEAMS WITH WEBHOOKSCHAPTER GOAL: This chapter covers topics like web services to Microsoft Teams with webhooks and explains how to create incoming webhook and outgoing webhooks with examples to guide the users.NO OF PAGES: 20SUB - TOPICS:1. Overview of Webservices and webhooks2. Connect web services to Microsoft Teams with webhooks3. Create incoming webhooks4. Exercise 1 - Create outgoing webhooks5. Overview of Connectors6. Use Office 365 Connectors in Microsoft Teams7. Exercise 2 - Create incoming webhooks8. Exercise 3 - Create and add Office 365 Connectors to teams9. ConclusionCHAPTER 8: EXTEND YOUR SOLUTION WITH MICROSOFT GRAPHCHAPTER GOAL: This chapter covers the topic, extend your solution with Microsoft Graph components with examples to guide the users.NO OF PAGES: 20SUB - TOPICS:1. Overview of Connectors2. Overview of Graph API3. Teams Graph APIs4. Graph API for Lifecycle Management5. AAD App Setup & App Permission6. Exercise 1 - Quick Start with AAD packaged solution7. Microsoft Graph: Teams app scenarios8. Exercise 2 – Manage Teams and Channel using AppCHAPTER 9: BUILDING TEAMS APPS USING SOLUTION ACCELERATORSCHAPTER GOAL: This chapter covers - Integrate Power Platform, App template and Teams Mobile Platform extensibility with examplesNO OF PAGES: 40SUB - TOPICS:1. Overview of Solution Accelerations2. PowerApps Integration3. Power Virtual Agents Integration4. Power Automate Integration5. Exercise 1 - Make Your PowerApps a Teams App6. App templates for Teams apps7. Exercise 2 - A simple FAQ bot that brings a human in the loop8. Teams Mobile Platform extensibility9. Exercise 3 – Message extension with Mobile extensibility10. ConclusionCHAPTER 10: SHAREPOINT AND TEAMS: BETTER TOGETHERCHAPTER GOAL: This chapter covers the topic SharePoint and teams better together via SharePoint Framework with examples.NO OF PAGES: 20SUB - TOPICS:1. What is SharePoint Framework2. Surfacing SharePoint Framework to Teams3. Package and distribute your SPFX webpart to Teams4. Exercise 1 – Create a SPFx webpart and distribute to Teams5. ConclusionCHAPTER 11: TEAMS LIFECYCLE MANAGEMENT AND DESIGN GUIDELINE FOR APPSCHAPTER GOAL: This chapter covers the topics Teams life cycle management and design guidelines with examples.NO OF PAGES: 30SUB - TOPICS:1. Overview of Deploy the apps2. Run and debug your Microsoft Teams app3. Packaging your app4. Deploy and publish your app5. Design tools and approach6. Best Practices and Examples7. Exercise 1 – Upload an app package to Microsoft Teams8. Exercise 2 – Publish your app to your organization9. Exercise 3 – Publish your app on AppStore10. Conclusion
Cyber Security on Azure
Prevent destructive attacks to your Azure public cloud infrastructure, remove vulnerabilities, and instantly report cloud security readiness. This book provides comprehensive guidance from a security insider's perspective.CYBER SECURITY ON AZURE supports cloud security operations and cloud security architects by supplying a path to clearly identify potential vulnerabilities to business assets and reduce security risk in Microsoft Azure subscription. This updated edition explores how to “lean-in” and recognize challenges with IaaS and PaaS for identity, networks, applications, virtual machines, databases, and data encryption to use the variety of Azure security tools. You will dive into Azure Cloud Security to guide cloud operations teams to become more security focused in many areas and laser focused on security configuration. New chapters cover Azure Kubernetes Service and Container security and you will get up and running quickly with an overview of Azure Sentinel SIEM Solution.WHAT YOU'LL LEARN* Understand enterprise privileged identity and security policies* "Shift left" with security controls in Microsoft Azure* Configure intrusion detection and alerts* Reduce security risks using Azure Security ServiceWHO THIS BOOK IS FORIT, cloud, and security administrators in AzureMARSHALL COPELAND is a cloud security architect focused on helping customers “shift left” with cloud security defenses in Azure public cloud using cloud-native services and third-party network security appliances. He uses Infrastructure as Code (IaC) with ARM templates or Terraform HCL to build cloud infrastructure and disaster recovery solutions. Marshall's Azure security design skills include Azure Sentinel, Security Center, Policy, Firewall and ACL networking, and a few open-source solutions such as ELK stack, Wireshark, and Snort. He partners with security operations to guide cloud investigations to enhance “blue team hunting” efficiencies.MATTHEW JACOBS is a system engineer focused on cloud architecture technologies needed to support identity management, security, and collaboration tool sets for small and medium businesses, including enterprise organizations. His work has focused on digital transformation, including on-premise only, hybrid cloud networks, and complete public cloud-only deployment. Matthew brings a hands-on cloud architecture approach for Identity Management (IAM) and enhanced engineering to enable business agility that secures and supports a global remote work force. His current work in the Nashville, Tennessee area includes Fortune 500 media, entertainment, and hospitality companies, and his work history extends into public cloud federal compliance requirements for the banking and healthcare industries.PART I: ZERO TRUST CLOUD SECURITYCHAPTER 1. REDUCE CYBERSECURITY VULNERABILITIES FROM THE IDENTITY LAYERIn this chapter you learn the foundation of Azure active directory and quickly expand on the different capabilities for custom domains to manage Azure Subscriptions and why Identity is the security perimeter in the cloud. Azure directly supports IAM (Identity Access Management), for any size organization as the IT cloud supports secure connection from any device and any location. In this chapter you gain insight into IAM challenges for blue team defense of cyber security attacks.· Azure cloud relations to: Azure Tenant, Azure Subscription, Azure ADo Azure tenant securityo Azure subscription securityo Azure API securityo Azure resource locks· Managing Azure Active Directory: Users, and Groups· Azure Active Directory OAuth, SAML, AD Connect· Security measures:o Azure Application Permission Scopes, consento Configure Multi-Factor Authenticationo Conditional Access Policies· Configure Azure AD Privileged Identity ManagementCHAPTER 2 AZURE NETWORK SECURITY CONFIGURATIONSoftware defined network is titled VNet in Azure and introduces new security challenges for cloud security architect when it comes to isolate data and still allow secure communication from valid users, applications and systems. In this chapter you learn security supported networking in Azure with the guides to present TCP/IP, protocol communication ports and what Azure security services are available to learn about notable tactics, techniques and procedures (TTPs) that can be exploited by Advanced Persistent Threats (APT). You learn VNet recommendations to mitigate misconfigurations and provide detection on Incidents of Compromise (IOC) like forensic evidence of potential intrusions.· Virtual Networks, VNets, Network Peering· NSG, Port vulnerability, OSI / TCP Model· Azure Firewall Configurations· Azure Front Door Service· Application Security Groups· Remote Access ManagementCHAPTER 3 REDUCE CYBERSECURITY VULNERABILITIES FROM IAAS AND DATAOperational frameworks and cyber security frameworks work hand-in-hand to support the business. The framework helps to prepare and enable steps to prevent penetration from globally attacks. In this chapter you learn through examples about advanced persistent threats (APT) using techniques, tactics and procedures to reduce risk to specific threats.· Harden Azure VMs· VM Security· VM Endpoint Security· VM OS security updates· Database configurations (Best Practices)o Authenticationo Auditingo SQL Advanced Threat Protection· Storage Accounts (data access)· Key Management (best practices)· Azure Files authentication· Shared Access Signatures (SAS)· HDInsight SecurityPART II: AZURE CLOUD SECURITY OPERATIONS (RED TEAM / BLUE TEAM)(150 pages)This section of the book is focused on identifying the vulnerabilities from a Red Team perspective (aka Black Hat) and how the Blue Team (White Hat) could defend from the attack. The topics are the same but the Red teams view to help train the Blue teams defense on specific cloud targets. During the chapters in Part II the reader is guided through many attack matrix from https://attack.mitre.org/ and C2Matrix examples of attackers and their attack techniques.CHAPTER 4 CONFIGURE AZURE MONITORING FOR BLUE TEAM HUNTINGIn this chapter readers learn about monitoring the availability of applications and services provide the insight on all Azure services from VM, to containers and cloud services specific to Microsoft Azure. Logs are divided into two functional types, Metrics, and logs. Azure has continued to expand insight by collecting this data and displaying in for alerts and management datapoints to respond appropriately.Data collected includes tenant and subscription data in attrition to all Azure resources. Metrics are near real time data (review using Metrics Explorer), reviews data at a specific point of time. Logs have different properties based on the type of logs. Streamed to an Analytics workspace for Alerting and review information over long periods of time.· Azure Monitor enablement· Logs sources and types of logs· Diagnostic logs & retention· Azure analytics· Privileged Identity Management Configuration· Monitor Privileged Access Best Practices· Manage API access Best Practices· Manage Azure subscription transfers (M&A activities)CHAPTER 5 AZURE SECURITY CENTER CONFIGURATIONAzure Security Center was introduced in the First Editions, and the reader continues their journey with a deep dive on considerations for reducing other security tools. You learn how to ingest log files from Azure environment and auto discover IaaS resources to reduce the “shadow IT” expansion. In this chapter the Cyber Security Kill Chain is front-and-center as you learn to configure alerts on known exploits. Again the reinforcement of the Attack Matrix is used to correlate and guide the Cloud Operations team into Cloud Security Operations.· Configuration cost (consolidation considerations)· Enable security:o Networko VMso Databaseo BLOBs· Data Protection· Configure Alerting· Central policy management with Security Center· Just in Time VM access with Security Center· Azure SentinelCHAPTER 6 AZURE KUBERNETES SERVICE AND CONTAINER SECURITYA NEW chapter in the second edition, takes the reader beyond the introduction to Kubernetes, it guides them on why containers are not secure by default. You learn container weakness and how to mitigate with security controls to secure Azure containers and the Azure Kubernetes Service (AKS).You learn to use Azure Security Center to identify the different Alerts from a Windows OS and Linux OS running in Azure IaaS configuration. Threat protection with Security Center expands the benefits of a cloud-native solution and you learn how using the security controls support your companies Cyber Security Framework.· Container Network Configuration· Authentication· Container isolation· AKS Security focus· Securing the container registry· Container vulnerability managementCHAPTER 7 SECURITY GOVERNANCE OPERATIONSA NEW chapter that uses many exercises to provide Azure Policy definition structure and readers learn how the policies take effect on users based on business rules. The exercises examples help readers evaluate the impact and what the logical evaluation of an Azure policy and how to customize the JSON policy definitions. Additional policies apply directly to Azure Kubernetes Services (AKS), to support the Information Security Officers team goals of improved security controls and reporting.· Azure Policies (overview)o Assignmentso Definitionso Blueprints· Compliance reports· Configure Azure Monitoro Diagnostic loggingo Log retentiono Vulnerability scanning· Data Managemento Classificationo Retentiono SovereigntyAPPENDIX A (10-20 PAGES)· Azure Penetration Testing ConfigurationAPPENDIX B (10-20 PAGES)· Configure an Azure Cloud Cyber Security lab for education
ML.NET Revealed
Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible.Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not.Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations.WHAT YOU WILL LEARN* Create a machine learning model using only the C# language* Build confidence in your understanding of machine learning algorithms * Painlessly implement algorithms * Begin using the ML.NET library software* Recognize the many opportunities to utilize ML.NET to your advantage* Apply and reuse code samples from the book* Utilize the bonus algorithm selection quick references available onlineWHO THIS BOOK IS FORDevelopers who want to learn how to use and apply machine learning to enrich their applicationsSUDIPTA MUKHERJEE is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.Chapter 01: Meet ML.NETChapter 02: The PipelineChapter 03: Handling DataChapter 04: RegressionsChapter 05: ClassificationsChapter 06: ClusteringChapter 07: Sentiment AnalysisChapter 08: Product RecommendationChapter 09: Anomaly DetectionChapter 10: Object Detection
Why AI/Data Science Projects Fail
RECENT DATA SHOWS THAT 87% OF ARTIFICIAL INTELLIGENCE/BIG DATA PROJECTS DON’T MAKE IT INTO PRODUCTION (VB STAFF, 2019), MEANING THAT MOST PROJECTS ARE NEVER DEPLOYED. THIS BOOK ADDRESSES FIVE COMMON PITFALLS THAT PREVENT PROJECTS FROM REACHING DEPLOYMENT AND PROVIDES TOOLS AND METHODS TO AVOID THOSE PITFALLS. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.* Preface* Introduction and Background* Project Phases and Common Project Pitfalls* Define Phase* Making the Business Case: Assigning Value to Your Project* Acquisition and Exploration of Data Phase* Model-Building Phase* Interpret and Communicate Phase* Deployment Phase* Summary of the five Methods to Avoid Common Pitfalls* References* Author Biography
Künstliche Intelligenz in der Automobilindustrie
Dieses Buch öffnet Ihnen die Augen, wie Künstliche Intelligenz die Automobilindustrie nachhaltig disrumpieren wird. Um diese Disruption zu meistern, müssen Automobilhersteller das volle Potential aus ihren Daten schöpfen, und in der Lage sein, täglich neue Dienste an ihre Kunden auszuspielen. Dieses Buch zeigt die dazu notwendigen Transformationen auf: Vom Aufbau einer tragfähigen Vision bis hin zur technologischen und organisatorischen Umsetzung im Unternehmen. Auf dieser Basis können sich die Automobilhersteller vom Blechbieger zum Techgiganten transformieren. In über 100 Fallbeispielen entlang der automobilen Wertschöpfungskette wird aufgezeigt, wo Künstliche Intelligenz einen Mehrwert liefern kann. Auf das autonome Fahren als wichtiger Enabler wird eingegangen sowie auf die wichtigsten Verfahren der Künstlichen Intelligenz, die für die Automobilindustrie relevant sind. Das Buch richtet sich an Entscheider in der Automobilindustrie, Studierende, Dozenten und alle, diesich ein Bild über eine der vielleicht größten industriellen Transformationen dieses Jahrhunderts machen möchten. Einführung: Die neue Wertschöpfungskette in der Automobilindustrie.- Auskuppeln: Prozesse optimieren mit Daten und künstlicher Intelligenz.- Gang einlegen: Mit KI die Einnahmequellen von morgen erschließen.- Beschleunigen: Die Transformation zum AI-first IT Giganten meistern.- Unter der Haube: Reale Fallbeispiele und Einsichten in den Transformationsalltag.
Electronics Projects with the ESP8266 and ESP32
Discover the powerful ESP8266 and ESP32 microcontrollers and their Wi-Fi communication. The ESP32 microcontroller features Bluetooth and BLE communication in addition to Wi-Fi. The book emphasizes practical projects and readers are guided through Wi-Fi and Bluetooth communication, mobile app design and build, ESP-NOW and LoRa communication, and signal generation.Projects throughout the book utilize the Wi-Fi functionality and processing power of the ESP microcontrollers. Projects are built in the Arduino IDE, so you don't need to download other programming software. Mobile apps are now ubiquitous, making the app build projects of the book very relevant, as are the web page design projects.In Electronics Projects with the ESP8266 and ESP32, you'll see how easy and practical it is to access information over the internet, develop web pages, build mobile apps to remotely control devices with speech recognition or incorporate Google Maps in a GPS route tracking app.You will· Build practical electronics projects with an ESP8266 or ESP32 microcontroller with Wi-Fi communication· Use the Wi-Fi function of the ESP8266 and ESP32 to update web pages· Communicate with your mobile phone or smart watch by Bluetooth Low Energy· Transmit and receive information to control remote devices over the internet· Understand the design and build of mobile apps for internet based applications· Apply your computer programming skills in C++, JavaScript, AJAX and JSON· Use WebSocket, MQTT brokers and IFTTT for fast two-way communication with webpagesWHO THIS BOOK IS FORThe target audience is for Makers and Tinkerers who want to build internet/intranet based applications with more powerful microcontrollers, such as the ESP8266 or ESP32. A level of C++ programming expertise with the Arduino IDE is assumed, although all sketches are fully described and comprehensively commented.Neil Cameron is an experienced analyst and programmer with a deep interest in understanding the application of electronics. Neil wrote the book 'Arduino Applied: Comprehensive Projects for Everyday Electronics'. He has previously taught at University of Edinburgh and Cornell University. Chapter 1: Internet radio• Station display and selection• Minimal internet radioChapter 2: Internet clock• WS2812 RGB LEDs responsive to sound• LED rings clock 24• Network Time ProtocolChapter 3: International weather station• Touch screen calibration• Painting on-screen• Weather data for several citiesChapter 4: Intranet camera• Save images to SD card• Load images on webpage• Stream images to webpageChapter 5: MP3 player• Control command for MP3 player• MP3 player control with Arduino• Infrared remote control of MP3 player• Creating sound tracks• Speaking clock• Voice recorderChapter 6: Bluetooth speakerChapter 7: ESP8266 local server• HTTP request• HTML code• XML HTTP requests, JavaScript and AJAXChapter 8: Updating a webpage• XML HTTP requests, JavaScript and AJAX• JSON• Accessing WWW data• Parsing text• Console log• Wi-Fi connectionChapter 9: WebSocket• Remote control of pan-tilt servo motors and WebSocket• Websocket and AJAX• Access images, time and sensor data over the internetChapter 10: Build an app• Control and feedback app• Install the app• Servo-robot control app• Speech recognition appChapter 11: App database and Google Maps• MIT App Inventor database• MIT App Inventor and Google MapsChapter 12: USB OTG apps• app receive• app transmit• app receive and transmitChapter 13: GPS and Google Maps• GPS position transmission• Validate transmission of GPS location• Improve GPS location signalChapter 14: Radio Frequency Communication• Transmitting and receiving text• Decode Remote Control Signals• Control Pan-Tilt Servos with RF Communication• Control relay with RF Communication• RelaysChapter 15: Signal generation• Signal generation• Digital to analog conversion• Generating waves• Port manipulation• 12-bit DACChapter 16: Signal generation with 555 integrated circuit• Monostable mode• Bistable mode• Astable mode• Variable duty cycle• 50% duty cycle• PWM mode• Function generator• Square wave to sine waveChapter 17: Measuring electricity• Analog to Digital Converter• Voltage meter• Resistance meter (ohmmeter)• Capacitance meter• Current meter (ammeter)• Current sensor• Solar panel and battery meter• Inductance meterChapter 18: Rotary encoder control• Interrupts• Debouncing• Square wave states• State switching• Incrementing a valueChapter 19: Saving data• Saving to EEPROM• Saving directly to ExcelChapter 20: Microcontrollers• Arduino Uno• Arduino Nano• Arduino Pro Micro• LOLIN (WeMos) D1 mini• Interrupts• Watchdog timer• ESP32• ESP32 analog input• ESP32 analog output• ESP32 pulse width modulation• ESP32 capacitive touch sensor• ESP32 Hall effect sensor• ESP32 RTC and sleep mode• ESP32 and interrupts• ESP32 Serial input• ESP32 Bluetooth communication• Wi-Fi communicationAppendixLibraries
Applied Data Science Using PySpark
Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade.Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines.By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets.WHAT YOU WILL LEARN* Build an end-to-end predictive model* Implement multiple variable selection techniques* Operationalize models* Master multiple algorithms and implementations WHO THIS BOOK IS FORData scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.RAMCHARAN KAKARLA is currently lead data scientist at Comcast residing in Philadelphia. He is a passionate data science and artificial intelligence advocate with five+ years of experience. He holds a master’s degree from Oklahoma State University with specialization in data mining. Prior to OSU, he received his bachelor’s in electrical and electronics engineering from Sastra University in India. He was born and raised in the coastal town of Kakinada, India. He started his career working as a performance engineer with several Fortune 500 clients including State Farm and British Airways. In his current role he is focused on building data science solutions and frameworks leveraging big data. He has published several papers and posters in the field of predictive analytics. He served as SAS Global Ambassador for the year 2015.SUNDAR KRISHNAN is passionate about artificial intelligence and data science with more than five years of industrial experience. He has tremendous experience in building and deploying customer analytics models and designing machine learning workflow automation. Currently, he is associated with Comcast as a lead data scientist. Sundar was born and raised in Tamil Nadu, India and has a bachelor's degree from Government College of Technology, Coimbatore. He completed his master's at Oklahoma State University, Stillwater. In his spare time, he blogs about his data science works on Medium.CHAPTER 1: SETTING UP THE PYSPARK ENVIRONMENTChapter Goal: Introduce readers to the PySpark environment, walk them through steps to setup the environment and execute some basic operationsNumber of pages: 20Subtopics:1. Setting up your environment & data2. Basic operationsCHAPTER 2: BASIC STATISTICS AND VISUALIZATIONSChapter Goal: Introduce readers to predictive model building framework and help them acclimate with basic data operationsNumber of pages: 30Subtopics:1. Basic Statistics2. data manipulations/feature engineering3. Data visualizations4. Model building frameworkCHAPTER 3: VARIABLE SELECTIONChapter Goal: Illustrate the different variable selection techniques to identify the top variables in a dataset and how they can be implemented using PySpark pipelinesNumber of pages: 40Subtopics:1. Principal Component Analysis2. Weight of Evidence & Information Value3. Chi square selector4. Singular Value Decomposition5. Voting based approachCHAPTER 4: INTRODUCTION TO DIFFERENT SUPERVISED MACHINE ALGORITHMS, IMPLEMENTATIONS & FINE-TUNING TECHNIQUESChapter Goal: Explain and demonstrate supervised machine learning techniques and help the readers to understand the challenges, nuances of model fitting with multiple evaluation metricsNumber of pages: 40Subtopics:1. Supervised:· Linear regression· Logistic regression· Decision Trees· Random Forests· Gradient Boosting· Neural Nets· Support Vector Machine· One Vs Rest Classifier· Naive Bayes2. Model hyperparameter tuning:· L1 & L2 regularization· Elastic netCHAPTER 5: MODEL VALIDATION AND SELECTING THE BEST MODELChapter Goal: Illustrate the different techniques used to validate models, demonstrate which technique should be used for a particular model selection task and finally pick the best model out of the candidate modelsNumber of pages: 30Subtopics:1. Model Validation Statistics:· ROC· Accuracy· Precision· Recall· F1 Score· Misclassification· KS· Decile· Lift & Gain· R square· Adjusted R square· Mean squared errorCHAPTER 6: UNSUPERVISED AND RECOMMENDATION ALGORITHMSChapter Goal: The readers explore a different set of algorithms – Unsupervised and recommendation algorithms and the use case of when to apply themNumber of pages: 30Subtopics:1. Unsupervised:· K-Means· Latent Dirichlet Allocation2. Collaborative filtering using Alternating least squaresCHAPTER 7: END TO END MODELING PIPELINESChapter Goal: Exemplify building the automated model framework and introduce reader to a end to end model building pipeline including experimentation and model trackingNumber of pages: 40Subtopics:1. ML FlowCHAPTER 8: PRODUCTIONALIZING A MACHINE LEARNING MODELChapter Goal: Demonstrate multiple model deployment techniques that can fit and serve variety of real-world use casesNumber of pages: 60Subtopics:1. Model Deployment using hdfs object2. Model Deployment using Docker3. Creating a simple Flask APICHAPTER 9: EXPERIMENTATIONSChapter Goal: The purpose of this chapter is to introduce hypothesis testing and use cases, optimizations for experiment-based data science applicationsNumber of pages: 40Subtopics:1. Hypothesis testing2. Sampling techniquesCHAPTER 10: OTHER TIPS: OPTIONALChapter Goal: This bonus chapter is optional and will offer reader some handy tips and tricks of the tradeNumber of pages: 20Subtopics:1. Tips on when to switch between python and PySpark2. Graph networks
Ontologies with Python
Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered.Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python.WHAT YOU WILL LEARN* Use Owlready2 to access and modify OWL ontologies in Python* Publish ontologies on dynamic websites* Perform automatic reasoning in PythonUse well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System)* Integrate Python methods in OWL ontologiesWHO IS THIS BOOK FORBeginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.Lamy Jean-Baptiste is a senior lecturer at Paris 13 University and a member of the LIMICS, a research lab focused on biomedical informatics. He is also the developer of the Owlready2 Python module that allows access to OWL ontologies. He has developed many research prototypes, and one of them (VCM iconic medical language) has been patented in the US, with three licenses sold to industrial partners.Lamy speaks regularly at artificial intelligence and medical informatics conferences, has written over 50 journal papers, and is a moderator on the Owlready forum on Nabbles. He was awarded the best paper award at MEDINFO 2019, the largest international conference in medical informatics.Chapter 1: Introduction1. Who is this book for?2. Why ontologies?3. Why Python?4. Why Owlready?5. Book outline6. AcknowledgementsChapter 2: Python Language: Adopt a Snake!1. Installing Python2. Starting Python3. Syntax4. Main datatypes5. Conditions (if)6. Loops (for)7. Generators8. Functions (def)9. Classes (class)10. Python modules11. Installing Owlready212. SummaryChapter 3: OWL Ontologies1. An ontology... what does it look like?2. Creating ontologies manually with the Protégé editor3. Example: An ontology of bacteria4. Creating a new ontology• Classes• Disjoints• Partitions • Data properties• Object properties• Restrictions• Union, intersection, and complement• Definitions (equivalent to relations)• Individuals• Other constructs5. Automatic reasoning6. Modeling exercises7. SummaryChapter 4: Accessing Ontologies in Python1. Importing Olwready2. Loading an ontology3. Imported ontologies4. Listing the content of the ontology5. Accessing to entities• Individuals• Relations• Classes• Existential restrictions• Properties6. Searching for entities7. Huge ontologies and disk cache8. Namespaces9. Modifying entity rendering as text10. Local directory of ontologies11. Reloading an ontology in the quadstore12. Example: Creating a dynamic website from an ontology13. SummaryChapter 5: Creating and Modifying Ontologies in Python1. Creating an empty ontology 2. Creating classes3. Creating properties4. Creating individuals5. Modifying entities: Relations and existential restrictions6. Creating entities within a namespace7. Renaming entities (refactoring)8. Multiple definitions and forward declarations9. Destroying entities10. Destroying an ontology11. Saving an ontology12. Importing ontologies13. Synchronization14. Example: Populating an ontology from a CSV file15. SummaryChapter 6: Constructs, Restrictions, Class Properties1. Creating constructs2. Accessing constructs parameters3. Restrictions as class properties4. Defined classes5. Example: Creating the ontology of bacteria in Python6. Example: Populating an ontology with defined classes7. SummaryChapter 7: Automatic Reasoning1. Disjoints2. Open-world assumption3. Reasoning in a closed world, or in a local closed world4. Inconsistent classes and inconsistent ontologies5. Restriction and reasoning on numbers and strings6. SWRL rules7. Example: An ontology-based decision support system8. SummaryChapter 8: Annotations, Multilingual Texts and Full Text Search1. Annotating entities2. Multilingual texts3. Annotating constructs4. Annotating properties and relations5. Creating new annotation classes6. Ontology metadata7. Full text search8. Example: Using DBpedia in Python• Loading DBpedia• A search engine for Dbpedia9. SummaryChapter 9: Using Medical Terminologies with PyMedTermino and UMLS1. UMLS2. Importing terminologies from UMLS3. Loading terminologies after initial importation4. Using ICD105. Using SNOMED CT6. Using UMLS unified concepts (CUI)7. Transcoding between terminologies8. Manipulating sets of concepts9. Importing all terminologies in UMLS10. Example: Linking the ontology of bacteria with UMLS11. Example: A multi-terminology browser12. SummaryChapter 10: Mixing Python and OWL1. Adding Python methods to OWL classes2. Associating a Python module to an ontology• Manual import• Automatic import3. Polymorphism with type inference4. Introspection5. Reading restrictions backward6. Example: using Gene Ontology and managing part-of relations7. Example: A “dating site” for proteins8. SummaryChapter 11: Working with RDF Triples and Worlds1. RDF triples2. Manipulating RDF triples with RDFlib3. Performing SPARQL requests4. Accessing RDF triples with Owlready5. Interrogating the SQLite3 database directly6. Creating several, isolated, world7. SummaryAnnex A: Description logicsAnnex B: Notations for formal ontologiesAnnex C: Reference manual
Deep Learning on Windows
Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will learn how Python can help you build deep learning models on Windows.Moving forward, you will build a deep learning model and understand the internal-workings of a convolutional neural network on Windows. Further, you will go through different ways to visualize the internal-workings of deep learning models along with an understanding of transfer learning where you will learn how to build model architecture and use data augmentations. Next, you will manage and train deep learning models on Windows before deploying your application as a web application. You’ll also do some simple image processing and work with computer vision options that will help you build various applications with deep learning. Finally, you will use generative adversarial networks along with reinforcement learning.After reading Deep Learning on Windows, you will be able to design deep learning models and web applications on the Windows operating system.WHAT YOU WILL LEARN* Understand the basics of Deep Learning and its historyGet Deep Learning tools working on Microsoft Windows* Understand the internal-workings of Deep Learning models by using model visualization techniques, such as the built-in plot_model function of Keras and third-party visualization tools* Understand Transfer Learning and how to utilize it to tackle small datasets* Build robust training scripts to handle long-running training jobs* Convert your Deep Learning model into a web application* Generate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network)* Understand the basics of Reinforcement LearningWHO THIS BOOK IS FORAI developers and enthusiasts wanting to work on the Windows platform.THIMIRA AMARATUNGA is an Inventor, a Senior Software Architect at Pearson PLC Sri Lanka with over 12 years of industry experience, and a researcher in AI, Machine Learning, and Deep Learning in Education and Computer Vision domains.Thimira holds a Master of Science in Computer Science with a Bachelor's degree in Information Technology from the University of Colombo, Sri Lanka. He has filed three patents to date, in the fields of dynamic neural networks and semantics for online learning platforms. Before this, Thimira has published two books on deep learning – ‘Build Deeper: The Deep Learning Beginners’ Guide’ and ‘Build Deeper: The Path to Deep Learning’.Thimira is also the author of Codes of Interest (www.codesofinterest.com), a portal for deep learning and computer vision knowledge, covering everything from concepts to step-by-step tutorials.LinkedIn: www.linkedin.com/in/thimira-amaratungaCHAPTER 1: WHERE TO START YOUR DEEP LEARNINGCHAPTER GOAL: Learn about what tools are available for deep learning and computer vision tasks. Learn about what consideration the reader needs to make about the tools, OS, and hardware.NO OF PAGES: 20SUB - TOPICS1. Can We Build Deep Learning Models on Windows?2. Programming Language – Python3. Package and Environment Management – Anaconda4. Python Utility Libraries for Deep Learning and Computer Vision5. Deep Learning Frameworks6. Computer Vision Libraries7. Optimizers and Accelerators8. What About Hardware?9. Recommended PC Hardware ConfigurationsChapter 2: Setting Up Your ToolsCHAPTER GOAL: Step-by-step instructions on how to install, configure and troubleshoot the required tools.NO OF PAGES: 35SUB - TOPICS:1. Installing Visual Studio with C++ Support2. Installing CMake3. Installing Anaconda Python4. Setting up the Conda Environment and the Python Libraries5. Installing TensorFlow6. Installing Keras multi-backend version7. Installing OpenCV8. Installing Dlib9. Verify Installations10. Optional Steps11. Troubleshooting12. SummaryChapter 3: Building Your First Deep Learning Model In WindowsCHAPTER GOAL: A step-by-step coding guide to building the first ‘hello world’ convolutional neural network image classification model.NO OF PAGES: 20SUB - TOPICS:1. What is the MNIST Dataset?2. The LeNet Model3. Let us Build Our First Model4. Running Our Model5. What Can You Do Next?Chapter 4: Understanding What We BuiltChapter Goal: Learn the internal workings of a convolutional neural network.NO OF PAGES: 20SUB - TOPICS:1. Digital Images2. Convolutions3. Non-Linearity Function4. Pooling5. Classifier (Fully Connected Layer)6. How Does This All Come Together?Chapter 5: Visualizing ModelsCHAPTER GOAL: Understand ways to visualize the internal workings of deep learning models, allowing the reader to use that knowledge to build complex models.No of pages: 20SUB - TOPICS:1. Why Visualizing Models is Useful2. Using the plot_model Function of Keras3. Using Netron to Visualize Model Structures4. Visualizing Convolutional FiltersCHAPTER 6: TRANSFER LEARNINGCHAPTER GOAL: Building deep learning systems that solves a practical problem is usually made hard due to the difficulty of collecting and managing training data. It is usually also hard to determine a model architecture for a given task from scratch. Here, the readers are introduced to the concept of transfer learning, which provides some solutions for those scenarios.NO OF PAGES: 45SUB - TOPICS:1. The Problem with Little Data2. Using Data Augmentations3. Build an Image Classification Model with Data Augmentation4. Bottleneck Features5. Using Bottleneck Features with a Pre-trained VGG16 Model6. Going Further with Model Fine-tuning7. Fine-tuning our VGG16 Model8. Trying out a Deeper Model – InceptionV3Chapter 7: Starting, Stopping. and Resuming LearningCHAPTER GOAL: Training deep learning models takes time: hours, maybe days. It may not be practical to perform the training in one go. This chapter shows ways on how to manage those situations.No of pages: 15SUB - TOPICS:1. Managing Long Running Training Jobs2. Using Model Checkpoints3. Resuming Training from a Checkpoint4. Knowing When to Stop Training5. Building a Robust Training ScriptChapter 8: Deploying Your Application as a Web ApplicationCHAPTER GOAL: Once the reader has built a deep learning model to perform a certain task, they should investigate options for deploying their model. This chapter gives some ideas for model deployment.NO OF PAGES: 20SUB - TOPICS:1. Getting Your Trained Models to Work2. Setting up Flask3. Designing Your Web Application4. Building Your Deep Learning Web Application5. Scaling Up Your Web ApplicationChapter 9: Having Fun with Computer VisionCHAPTER GOAL: A chapter on some basic image processing and computer vision options, techniques, and tricks that would help the reader when building various applications with deep learning.NO OF PAGES: 20SUB - TOPICS:1. What we Need?2. Basics of Working with Images3. Working with Video – Using Webcams4. Working with Video – Using Video Files5. Detecting Faces in Images6. Detecting Faces in Video7. Simple Real-time Deep Learning Object IdentificationChapter 10: Introduction to Generative Adversarial NetworksCHAPTER GOAL: Introducing the idea of Generative Adversarial Networks and their capabilities. Giving a small taste of what they can do with few coding examples.No of pages: 30SUB - TOPICS:1. Can an AI be Creative?2. The Story of the Artist and the Art Critic3. Generative Adversarial Networks4. Generating Handwritten Digits with DCGAN5. Can We Generate Something More Complex?6. What Else Can GANs Do?Chapter 11: Basics of Reinforcement LearningCHAPTER GOAL: Introduce the concept of Reinforcement Learning and how it can be applied to train models to solve problems and introduce the concept of game AI programming.NO OF PAGES: 25SUB - TOPICS:1. What is Reinforcement Learning2. What is OpenAI Gym?3. Setting up OpenAI Gym4. Solving the CartPole Problem5. Solving the MountainCar Problem6. What Can You Do Next?
Machine Learning and AI for Healthcare
This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things.You will understand how machine learning can be used to develop health intelligence–with the aim of improving patient health, population health, and facilitating significant care-payer cost savings.WHAT YOU WILL LEARN* Understand key machine learning algorithms and their use and implementation within healthcare* Implement machine learning systems, such as speech recognition and enhanced deep learning/AI* Manage the complexities of massive data* Be familiar with AI and healthcare best practices, feedback loops, and intelligent agentsWHO THIS BOOK IS FORHealth care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.ARJUN PANESAR is the founder of Diabetes Digital Media (DDM), the world’s largest diabetes community and provider of evidence-based digital health interventions. He holds an honors degree (MEng) in computing and artificial intelligence from Imperial College, London. He has a decade of experience in big data and affecting user outcomes, and leads the development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precision patient care to patients, health agencies, and governments worldwide.Arjun’s work has received international recognition and was featured by the BBC, Forbes, New Scientist, and The Times. He has received innovation, business, and technology awards, including being named the top app for prevention of type 2 diabetes.Arjun is an advisor to the Information School, at the University of Sheffield, Fellow to the NHS Innovation Accelerator, and was recognized by Imperial College as an Emerging Leader in 2020 for his contribution and impact to society.Chapter 1: Introduction: Learning for HealthcareChapter Goal: Introduction to book and topics to be coveredNo of pages 10Sub -Topics1. What is AI, data science, machine and deep learning2. The case for learning from data3. Evolution of big data/learning/Analytics 3.04. Practical examples of how data can be used to learn within healthcare settings5. ConclusionChapter 2: Big DataChapter Goal: To understand data required for learning and how to ensure valid data for outcome veracityNo of pages: 35Sub - Topics1. What is data, sources of data and what types of data is there? little vs big data and the advantages/disadvantages with such data sets. Structured vs. unstructured data.2. Massive data - management and complexities3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information4. How to use big data for learning (use cases)5. Turning data into information – how to collect data that can be used to improve health outcomes and examples of how to collect such data6. Challenges faced as part of the use of big data7. Data governanceChapter 3: What is Machine learning?Chapter Goal: To introduce machine learning, identify/demystify types of learning and provide information of popular algorithms and their applicationsNo of pages: 45Sub - Topics:1. Introduction – what is learning?2. Differences/similarities between: what is AI, data science, machine learning, deep learning3. History/evolution of learning4. Learning algorithms – popular types/categories, complex examples of machine learning models, applications and their mathematical basis5. Software(s) used for learning6. Code samplesChapter 4: Machine Learning in HealthcareChapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settingsNo of pages: 50Sub - Topics:1. Understanding Tasks, Performance and Experience to optimize algorithms and outcomes2. Identification of algorithms to be used in healthcare applications for: predictive analysis, perspective analysis, inference, modeling, probability estimation, NLP etc and common uses3. Real-time analysis and analytics4. Machine learning best practices5. Neural networks, ANNs, deep learning6. Code samplesChapter 5: Evaluating Learning for IntelligenceChapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysisNo of pages: 301. How to evaluate machine learning systems2. Methodologies for evaluating outputs3. Improving your intelligence4. Advanced analytics5. Real-world examples of evaluationsChapter 6: Ethics of intelligenceChapter Goal: To understand the hurdles that must be addressed in AI/machine learning and also overcome on both a micro- and macro-level to enable enhanced health intelligenceNo of pages: 251. The benefits of big data and machine learning2. The disadvantages of big data and machine learning – who owns the data, distributing the data, should patients/people be told what the results are (e.g. data demonstrates risk of cancer)3. Data for good, or data for bad?4. Topics that require addressing in order to ensure ease, efficiency and safety of outputs5. Do we need to govern our intelligence?6. Example: COVID-19 response and data/privacy sharingChapter 7: The Future of HealthcareChapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systemsNo of pages: 301. Evidence-based medicine2. Patient data as the evidence base3. Healthcare disruption fueling innovation4. How generalisations on precise audiences enables personalized medicine5. Impact of data and IoT on realizing personalized medicine6. AI ethics7. ConclusionChapter 8: Case studiesChapter Goal: Real world applications of AI and machine/deep learning in healthcareNo of pages: 501. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes2. COVID-related case studies: how data was used, how rapid interventions were deployed, agile development methodolodies
Cloud-Based Microservices
Use this field guide as you transform your enterprise to combine cloud computing with a microservices architecture.The recent surge in the popularity of microservices in software development is mainly due to the agility it brings and its readiness for the cloud. The move to a microservices architecture on the cloud involves a gradual evolution in software development. Many enterprises are embarking on this journey, and are now looking for architects who are experienced in building microservices-based applications in the cloud.A master architect should be able to understand the business, identify growth hurdles, break a monolith, design microservices, foresee problems, overcome challenges, change processes, decipher CSP services, strategize cloudification, adopt innovations, secure microservices, prototype solutions, and envision the future. CLOUD-BASED MICROSERVICES provides you with the information you need to be successful in such an endeavor.WHAT YOU WILL LEARN* Be familiar with the challenges in microservices architecture and how to overcome them* Plan for a cloud-based architecture* Architect, build, and deploy microservices in the cloud* Know how security, operations, and support change in this architectureWHO THIS BOOK IS FOREngineers, architects, and those in DevSecOps attempting to move their enterprise software to take advantage of microservices and the cloud and be more nimbleCHANDRA RAJASEKHARAIAH has led multi-million dollar enterprise initiatives in cloud-based microservice development. For the past five years, he has also migrated giant enterprise monoliths to microservices-based applications on the cloud. He has more than 20 years of experience in the software engineering industry as a principal, enterprise architect, solutions architect, and software engineer. His experience includes multiple domains—retail, e-commerce, telecommunications, telematics, travel, electronic payments, automobile—and gives him a broad base to draw parallels, abstract problems, and create innovative solutions. He enjoys architecting, delivering, and supporting enterprise products.PREFACEWhat This Book isWhat This Book is NotCHAPTER 1: CASE STUDY: ENERGENCE CO.Managing Production and DistributionHardware and Software InfrastructureMonolithic Software SolutionsGrowth Opportunities and ObjectivesNext StepsFurther Related ReadingSummaryPoints to PonderCHAPTER 2: MICROSERVICES: WHAT AND WHY?OriginsMicroservices Architecture in a NutshellSuccessful Implementation of MicroservicesOrchestration and ChoreographyMicroservices Migration Plan for EnergenceBreaking a Monolith into ModulesBreaking Modules into Sub-modulesEstablishing Microservices ArchitectureAdvantages and Gains with MicroservicesFurther Related ReadingSummaryPoints to PonderCHAPTER 3: ARCHITECTURAL CHALLENGESIdentifying and Classifying ChallengesAC1: Dispersed Business LogicAC2: Lack of Distributed TransactionsOrchestrated DomainsChoreographed DomainsAC3: Inconsistent Dynamic Overall StateChallenges in Exchanging Data between MicroservicesProblems with ShardingAC4: Difficulty in Gathering Composite DataAC5: Difficulty in Debugging Failures and FaultsAC6: The v2 Dread – Difficulty in EvolvingFurther Related ReadingSummaryPoints to PonderCHAPTER 4: OVERCOMING ARCHITECTURAL CHALLENGESService CatalogSagas (Long-Running Transactions)Ignoring ErrorsCompensating Errors InlineCompensating Errors OfflineImplementing SagasMaintaining Global StatesThe Scenario of Dynamic Overall StateIntermittent-Peek OptionAlways-Listening OptionOther Options and Larger QuestionsCentralized ViewObservabilityContract TestingFurther Related ReadingSummaryPoints to PonderCHAPTER 5: PROCESS CHANGESContinuous IntegrationBuild and Integration EnvironmentsAutomated TestingPerformance TestingContinuous DeliveryInfrastructure as CodDevSecOpsFurther Related ReadingSummaryPoints to PonderCHAPTER 6: CLOUDIFICATION – STRATEGYOverall Setup for Microservices in CloudNetworking and ConnectivityRegions and ZonesComputeIntegrationDatabases and Traditional DatastoresSpecial-Purpose DatastoresCost AnalysisSummaryPoints to PonderCHAPTER 7: CLOUDIFICATION – CORE CONCEPTSVirtualization and ContainerizationContainer OrchestrationService MeshesTraffic ControlEstablishing and Securing CommunicationBuilding Overall ObservabilityChallenges and State of the Art of Service MeshesFaaS, aka, ServerlessStorage and Integration ServicesStorage ServicesIntegration ServicesFurther Related ReadingSummaryPoints to PonderCHAPTER 8: SECURING MICROSERVICES ON CLOUDSecuring MicroservicesReducing the Attack SurfaceSecuring ServicesSecuring Outgoing CommunicationSecuring Microservices on CloudAPI Gateways and Load BalancersIAM of CSPsSecuring Inter-Service CommunicationProcessing IntegrityTrusted BinariesTrusted ExecutionAvailabilityDR-Disaster RecoveryMulti-region SolutionsFurther Related ReadingSummaryPoints to PonderCHAPTER 9: MICROSERVICES, HERE AND BEYONDTrendsSupport and OperationsMicroservices on CloudChanging Security LandscapeAlternate ThoughtsMonoliths are Dead, Long Live the MonolithIN CLOSINGBIBLIOGRAPHYAPPENDIXCOMPARING CSPS
Immersive 3D Design Visualization
Discover the methods and techniques required for creating immersive design visualization for industry. This book proposes ways for industry-oriented design visualization from scratch. This includes fundamentals of creative and immersive technology; tools and techniques for architectural visualization; design visualization with Autodesk Maya; PBR integration; and texturing, material design, and integration into UE4 for immersive design visualization.You’ll to dive into design and visualization, from planning to execution. You will start with the basics, such as an introduction to design visualization as well as to the software you will be using. You will next learn to create assets such as virtual worlds and texturing, and integrate them with Unreal Engine 4. Finally, there is a capstone project for you to make your own immersive visualization scene.By the end of the book you’ll be able to create assets for use in industries such as game development, entertainment, architecture, design engineering, and digital education.WHAT YOU WILL LEARN* Gain the fundamentals of immersive design visualization* Master design visualization with Autodesk Maya* Study interactive visualization with UE4* Create your immersive design portfolio WHO THIS BOOK IS FORBeginning-intermediate learners from the fields of animation, visual art, and computer graphics as well as design visualization, game technology, and virtual reality integration.DR. ABHISHEK KUMAR is an assistant professor in the Department of Computer Science at the Institute of Science at Banaras Hindu University. He is an Apple Certified Associate, Adobe Education Trainer, and certified by Autodesk. He is actively involved in course development in animation and design engineering courses for various institutions and universities as they will be a future industry requirement. Dr. Kumar has published a number of research papers indexed in Scopus and Web of Science and covered a wide range of topics in various digital scientific areas (image analysis, visual identity, graphics, digital photography, motion graphics, 3D animation, visual effects, editing, and composition).He holds eight patents in the field of computer science, design and IoT. Dr. Kumar has completed professional studies related to animation, computer graphics, virtual reality, stereoscopy, filmmaking, visual effects, and photography from Norwich University of the Arts, the University of Edinburgh, and Wizcraft MIME and FXPHD, Australia.He is passionate about the media and entertainment industry, and has directed two animation short films. Dr. Kumar has trained more than 50,000 students across the globe from 153 countries (top five: India, Germany, United States, Spain, and Australia). His alumni have worked on movies such as Ra-One, Krissh, Dhoom, Life of Pi, the Avengers series, the Iron Man series, GI Joe 3D, 300, Alvin and the Chipmunks, Prince of Persia, Titanic 3D, the Transformers series, Bahubali 1 and 2, London Has Fallen, Warcraft, Aquaman 3D, Alita, and more.CHAPTER 1: DESIGN FOR CREATIVE AND IMMERSIVE TECHNOLOGY• Scope of this book• Topics covered• Design visualization• Emerging technologies (VR, AR, and MR)CHAPTER 2: TOOLS FOR ARCHITECTURAL VISUALIZATION• MAYA for design visualization• Substance for PBR texturing• Design visualization gamification (UE4)CHAPTER 3: 3D DESIGN WITH AUTODESK MAYA• Basics of modelling• Basics of unwrapping• Basics of Substance PainterCHAPTER 4: INTERACTIVE VISUALIZATION WITH UE4• Interface of UE4• Exploring toolsCHAPTER 5: CREATING VIRTUAL WORLDS• Modelling assetsCHAPTER 6: UNWRAPPING OUR ASSETS• Introduction to unwrapping• Unwrapping assetsCHAPTER 7: LIGHTMAP ANALYSIS AND CORRECTION• Creating Lightmap UVs• Static vs. dynamic lighting• Lightmap analysis, correction, and padding• Shader analysis and tweakingCHAPTER 8: PBR INTEGRATED TEXTURING• Importing and baking maps• Texturing various assetsCHAPTER 9: MATERIAL DESIGN AND INTEGRATION• Exporting for UE4• Importing into UE4• Material setupCHAPTER 10: REAL-TIME/EMISSIVE MATERIALS• Emissive workflow in Substance Painter• Emissive workflow in UE4CHAPTER 11: INTERACTION DESIGN IN VR ENGINE• Importing 3D assets• Object properties editorCHAPTER 12: UNREAL® ENGINE 4 FOR LEVEL DESIGN• Creating level• Documenting problems and errorsCHAPTER 13: DESIGN VISUALIZATION CAPSTONE PROJECT: TESTING AND FIXING ERRORS• Fixing errorsCHAPTER 14: DESIGN VISUALIZATION CAPSTONE PROJECT: AESTHETIC DEVELOPMENT• Completing level design• Lighting our sceneCHAPTER 15: IMMERSIVE DESIGN PORTFOLIO• Cleaning up• Testing with VR headsets• Thoughts and suggestions
Exploring Windows Presentation Foundation
Use the Windows Presentation Foundation (WPF) technology to develop Windows applications using C# and XAML for design. This book will get you through not only the basics, but also some of the more advanced concepts of WPF in .NET 5.The book starts with basic concepts such as window, page, text box, and message box as well as a sequence of common events and event handling in WPF. You will learn how to use various elements in WPF and deal with them in .NET 5. You will understand how to work with files and access them in WPF along with binding and MVVM (Model-View-View-Model). You will learn how to retrieve data from APIs, work in XAML, and understand where design and style properties should be applied in WPF.After reading this book you will be able to work on WPF and apply its concepts in .NET 5, .NET core, and the .NET framework.WHAT YOU WILL LEARN* Understand the basics of WPF: click event, inputs, and general setup* Work with WPF interface events and handling* Know how file handling works in WPF* Retrieve data from APIs in a modern wayWHO THIS BOOK IS FORDevelopers with basic knowledge of C#.TAURIUS LITVINAVICIUS is a businessman and technology expert based in Lithuania who has worked with various organizations in building and implementing projects in software development, sales, and other fields of business. He is responsible for technological improvements, development of new features, and general management. Taurius is also the director at the Conficiens solutio consulting agency where he supervises the development and maintenance of various projects and activities.CHAPTER 1 – GETTING STARTEDChapter goal: Understand the basic concepts of WPF, this will help the reader to easily practice the next concepts.Section 1 - Button and click eventSection 2 – Window and PageSection 3 – Text boxSection 4 – Message boxSection – Quick-exampleSection – Quick-exerciseCHAPTER 2 – EVENTSChapter goal: Understand the most common events and event handling in WPFSection 1 – Application eventsSection 2 – Mouse eventsSection 3 – Keyboard eventsSection 4 – Window eventsSection – Quick-exampleSection – Quick-exerciseCHAPTER 3 – UI ELEMENTSChapter goal: This chapter teaches how to use various elements in WPF, as well as how to deal with them in general.Section 1 – Progress barSection 2 – TabsSection 3 – Radio buttonSection 4 – Check boxSection 5 – SliderSection 6 – ImageSection 7 – Media elementSection 8 – MenuSection 9 – List viewSection 10 – Web browserSection 11 – CanvasSection 12 – Generate elements in C#Section 13 – Background tasksCHAPTER 4 – FILESChapter goal: Understand how to access files and save files in WPFSection 1 – Pick and saveSection – Quick-example (Image auto-resize)Section – Quick-exercise (Assign file name)CHAPTER 5 – BINDINGS AND MVVMChapter goal: Understand the concept of MVVM structure in WPFSection 1 – What is MVVM structure?Section 2 – Element to element bindingSection 3 – Introducing ViewModelSection 4 – Implementing modelsSection – Quick-exampleSection – Quick-exerciseCHAPTER 6 – XAMLChapter goal: Understand where design and style properties should be applied in WPFSection 1 – Window size and sizeSection 2 – Style templateSection – Quick-example (custom message box)Section – Quick-exercise
AI In The Age Of Cyber-Disorder
The rise of Artificial Intelligence applications is accelerating the pace and magnitude of the political, securitarian, and ethical challenges we are now struggling to manage in cyberspace and beyond. So far, the relationship between Artificial Intelligence and cyberspace has been investigated mostly in terms of the effects that AI could have on the digital domain, and thus on our societies. What has been explored less is the opposite relationship, namely, how the cyberspace geopolitics can affect AI. Yet, AI applications have so far suffered from growing unrest, disorder, and lack of normative solutions in cyberspace. As such, from algorithm biases, to surveillance and offensive applications, AI could accelerate multiple growing threats and challenges in and through cyberspace. This report by ISPI and The Brookings Institution is an effort to shed light on this less studied, but extremely relevant, relationship.
Handbuch moderner Softwarearchitektur
SOFTWAREARCHITEKTUR ZEITGEMÄSS UND PRAGMATISCH GEPLANT * Architektonische Muster: Das technische Fundament für viele architektonische Entscheidungen * Komponenten: Identifizierung, Kopplung, Kohäsion, Partitionierung und Granularität * Architekturstile wie Microkernel, SOA, Microservices u.v.m. und ihre architektonischen Eigenschaften * Softwarearchitektur als Engineering-Disziplin: mit wiederhol- und messbaren Ergebnissen zu stabilen Architekturen Mark Richards und Neal Ford — Praktiker mit Erfahrung aus erster Hand, die seit Jahren das Thema Softwarearchitektur unterrichten —, betrachten Softwarearchitektur vor dem Hintergrund der Entwicklungen, Innovationen und Herausforderungen des letzten Jahrzehnts. Sie konzentrieren sich auf Architekturprinzipien, die für alle Technologie-Stacks gelten. Angehende und erfahrene Architekten finden in diesem Buch umfassende Informationen zu architektonischen Merkmalen und Architekturstilen, zur Bestimmung von Komponenten, zur Diagrammerstellung und Präsentation, zu evolutionärer Architektur und vielen weiteren Themen. Die Autoren verstehen Softwarearchitektur als Engineering-Disziplin: mit wiederhol- und messbaren Ergebnissen und konkreten Kennzahlen für stabile Softwarearchitekturen.
Error Correction Coding
PROVIDING IN-DEPTH TREATMENT OF ERROR CORRECTIONError Correction Coding: Mathematical Methods and Algorithms, 2nd Editionprovides a comprehensive introduction to classical and modern methods of error correction. The presentation provides a clear, practical introduction to using a lab-oriented approach. Readers are encouraged to implement the encoding and decoding algorithms with explicit algorithm statements and the mathematics used in error correction, balanced with an algorithmic development on how to actually do the encoding and decoding. Both block and stream (convolutional) codes are discussed, and the mathematics required to understand them are introduced on a “just-in-time” basis as the reader progresses through the book.The second edition increases the impact and reach of the book, updating it to discuss recent important technological advances. New material includes:* Extensive coverage of LDPC codes, including a variety of decoding algorithms. * A comprehensive introduction to polar codes, including systematic encoding/decoding and list decoding. * An introduction to fountain codes. * Modern applications to systems such as HDTV, DVBT2, and cell phones Error Correction Coding includes extensive program files (for example, C++ code for all LDPC decoders and polar code decoders), laboratory materials for students to implement algorithms, and an updated solutions manual, all of which are perfect to help the reader understand and retain the content.The book covers classical BCH, Reed Solomon, Golay, Reed Muller, Hamming, and convolutional codes which are still component codes in virtually every modern communication system. There are also fulsome discussions of recently developed polar codes and fountain codes that serve to educate the reader on the newest developments in error correction.TODD K. MOON is a Professor in the Electrical and Computer Engineering Department at Utah State University. His research interests include information systems (communications, signal processing, and controls) and the application of principles of mathematics to problems involving the transmission, extraction, modeling, compression, or analysis of signals. He is the author of numerous journal and conference articles and graduate level texts on signal processing and error correction coding. Preface xviiList of Program Files xxiiiList of Laboratory Exercises xxixList of Algorithms xxxiList of Figures xxxiiiList of Tables xliList of Boxes xliiiAbout the Companion Website xlvPART I INTRODUCTION AND FOUNDATIONS 11 A Context for Error Correction Coding 3PART II BLOCK CODES 692 Groups and Vector Spaces 713 Linear Block Codes 934 Cyclic Codes, Rings, and Polynomials 1235 Rudiments of Number Theory and Algebra 1796 BCH and Reed–Solomon Codes: Designer Cyclic Codes 2417 Alternate Decoding Algorithms for Reed–Solomon Codes 2998 Other Important Block Codes 3719 Bounds on Codes 40710 Bursty Channels, Interleavers, and Concatenation 42511 Soft-Decision Decoding Algorithms 439PART III CODES ON GRAPHS 45312 Convolutional Codes 45513 Trellis-Coded Modulation 545PART IV ITERATIVELY DECODED CODES 58914 Turbo Codes 59115 Low-Density Parity-Check Codes: Introduction, Decoding, and Analysis 63716 Low-Density Parity-Check Codes: Designs and Variations 717PART V POLAR CODES 77717 Polar Codes 779PART VI APPLICATIONS 88518 Some Applications of Error Correction in Modern Communication Systems 887PART VII SPACE-TIME CODING 89919 Fading Channels and Space-Time Codes 901Index 939
50 Arten, Nein zu sagen
PFLICHTLEKTÜRE FÜR PRODUCT OWNER UND ALLE, DIE IHR STAKEHOLDER-MANAGEMENT EFFEKTIVER GESTALTEN MÖCHTEN * Einfaches Fünf-Schritte-Modell, um effektiv »Nein« zu sagen * Neun Kategorien mit insgesamt 50 Facetten von Nein * Mit vielen Denkanstöße, praktischen Tipps und Beispielen, in denen die richtige Art und Weise, »Nein« zu sagen, aufgezeigt wird "Nein" zu sagen ist nicht immer einfach und kann nicht immer auf die gleiche Weise erfolgen. Es ist auch nicht immer möglich, "Nein" zu sagen. Doch "Nein" zu sagen, bedeutet auch ein "Ja" zu den richtigen Dingen. Das ist eine Grundkompetenz, um die Effektivität als Product Owner, einer der Schlüsselrollen innerhalb von Scrum, zu steigern. Wie gehen Product Owner mit den Stakeholdern um? Und wie gibt man ein effektives "Nein"? Das Buch gibt Antworten auf diese und verschiedene andere Fragen – und zwar auf 50 verschiedene Arten. Die vielen konkreten Beispiele und Erkenntnisse aus der jahrelangen Erfahrung der Autoren als professionelle Scrum-Trainer und Berater bieten wertvolle Tipps und Handlungsoptionen, um sofort loszulegen.