Allgemein
Leveling Up with SQL
Learn to write SQL queries to select and analyze data, and improve your ability to manipulate data. This book will help you take your existing skills to the next level.Author Mark Simon kicks things off with a quick review of basic SQL knowledge, followed by a demonstration of how efficient SQL databases are designed and how to extract just the right data from them. You’ll then learn about each individual table’s structure and how to work with the relationships between tables. As you progress through the book, you will learn more sophisticated techniques such as using common table expressions and subqueries, analyzing your data using aggregate and windowing functions, and how to save queries in the form of views and other methods. This book employs an accessible approach to work through a realistic sample, enabling you to learn concepts as they arise to improve parts of the database or to work with the data itself.After completing this book, you will have a more thorough understanding of database structure and how to use advanced techniques to extract, manage, and analyze data.WHAT WILL YOU LEARN* Gain a stronger understanding of database design principles, especially individual tables* Understand the relationships between tables* Utilize techniques such as views, subqueries, common table expressions, and windowing functionsWHO IS THIS BOOK FOR:SQL Databases users who want to improve their knowledge and techniques.MARK SIMON has been involved in training and education since the beginning of his career. He started as a teacher of mathematics, but quickly pivoted into IT consultancy and training because computers are much easier to work with than high school students. He has worked with and trained in several programming and coding languages, and currently focuses mainly on web development and database languages. When not involved in work, you will generally find him listening to or playing music, reading, or just wandering about.Chapter 1: Getting Ready.- Chapter 2: Working with Table Design.- Chapter 3: Table Relationships and Working With Joins.- Chapter 4: Working with Calculated Data.- Chapter 5: Aggregating Data.- Chapter 6: Creating and Using Views and Friends.- Chapter 7: Working With Subqueries and Common Table Expressions.- Chapter 8: Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.
Domain-Driven Transformation
Domain-Driven Transformation. Monolithen und Microservices zukunftsfähig machen. September 2023.In den letzten Jahrzehnten wurde viel Software entwickelt, die wir heute modernisieren und zukunftsfähig machen müssen. Domain-Driven Design (DDD) eignet sich hervorragend, um große Legacy-Systeme in Microservices zu zerlegen oder zu wartbaren Monolithen umzubauen.Mit ihrer Methode »Domain-Driven Transformation« haben Carola Lilienthal und Henning Schwentner einen umfassenden Ansatz geschaffen, um sowohl auf strategischer als auch auf technischer und teamorganisatorischer Ebene architektonisch erodierte Softwaresysteme zu transformieren. Dabei spannen sie den Bogen von der Analyse der fachlichen Prozesse und der Zerlegung in Bounded Contexts bis hin zu Domain-Driven Refactorings und deren Umsetzung in agilen Teams. Schließlich geben sie der Leserschaft eine Anleitung, wie der Transformationsprozess abhängig vom Zustand der vorhandenen Architektur gestaltet werden sollte. Im Einzelnen werden behandelt:Domain-Driven DesignCollaborative ModelingTeam TopologiesMicroservices und MonolithenModularity Maturity Index (MMI)Domain-Driven RefactoringsPriorisierung und Durchführung der UmbaumaßnahmenSie lernen anhand zahlreicher Beispiele verschiedene Möglichkeiten der Transformation bis hinunter in den Code kennen, die Sie schrittweise in Ihre Alltagspraxis übernehmen können, um die Wartbarkeit Ihrer Legacy- Systeme effektiv und schnell zu verbessern.Leseprobe Inhaltsverzeichnis (PDF-Link)Leseprobe Kapitel 1 (PDF-Link)
Basiswissen Usability und User Experience (2. Auflg.)
Know-how für Usability-Experten und -Anfänger: Aus- und Weiterbildung zum UXQB® Certified Professional for Usability and User Experience (CPUX) – Foundation Level (CPUX-F).Gebrauchstaugliche Produkte, die ein positives Benutzererlebnis (User Experience) erzeugen, sind das Ergebnis eines systematischen Prozesses. Das Know-how der Projektbeteiligten über die Konzepte, den Prozess und die notwendigen Prozessergebnisse rund um »Usability und User Experience« bildet hierbei die Basis für eine hohe menschzentrierte Qualität des Projektergebnisses.Die Autoren geben eine fundierte Einführung und einen praxisorientierten Überblick über die Kompetenzfelder »Usability und User Experience« und deren Zusammenspiel. Zahlreiche Beispiele zu Gestaltungsprinzipien, Gestaltungsregeln, Design Patterns, Erfordernissen (User Needs) und Nutzungsanforderungen (User Requirements) erläutern die methodischen Grundlagen. Im Einzelnen werden behandelt:Aus dem Inhalt:Aktivitäten der menschzentrierten GestaltungGrundlegende Begriffe und KonzepteMenschzentrierte Gestaltung planenDen Nutzungskontext verstehen und festlegenNutzungsanforderungen festlegenLösungen gestalten, die Nutzungsanforderungen erfüllenGestaltungslösungen evaluierenDiese 2., überarbeitete und aktualisierte Auflage wurde um neue Themen wie User-Interface-Spezifikation, ethisches Design und nachhaltiges Design erweitert. 157 Prüfungsfragen mit Lösungen und Erläuterungen helfen dabei, das Gelernte zu vertiefen.Das Buch umfasst alle Inhalte des UXQB®-Lehrplans zum CPUX-F (Version 4.01, 2023) und eignet sich daher als kompaktes Grundlagenwerkbestens zur Prüfungsvorbereitung, für die Anwendung in der Praxis und als Lehrbuch an Hochschulen.Zu den Autoren:Thomas Geis ist Geschäftsführer der ProContext Consulting GmbH und seit 25 Jahren Vollzeit im Arbeitsgebiet Usability-Engineering tätig. Er ist Vorsitzender des International Usability and User Experience Qualification Board (UXQB) und Gründer des Arbeitskreises Qualitätsstandards des deutschen Berufsverbands der Usability und User Experience Professionals (German UPA), Leiter des ISO-Ausschusses „Common Industry Format for Usability“, Editor von ISO 9241-110 „Grundsätze der Dialoggestaltung“ und von ISO 25060 „Common Industry Format (CIF) for Usability – General Framework for Usability-related Information“, Leiter des DIN-Ausschusses „Benutzungsschnittstellen“ sowie Träger des Usability Achievement Award der German UPA (2013).Guido Tesch ist Senior Consultant Human-Centered Design bei der ProContext Consulting GmbH in Köln und seit 2001 als Usability und UX Professional tätig mit Schwerpunkten in Konzeption, UX Architecture, UI Design, UI Guidelines, User Research, Anforderungsanalyse, Usability Testing und HCD-Prozesse. Er arbeitet im DIN-Ausschuss zur Erarbeitung der zentralen Normen rund um Usability und UX mit, ist seit 2016 National Expert des Berufsverbandes German UPA und ist zertifiziert in CPUX-F (Foundation Level, Trainer), CPUX-DS (Designing Solutions, Trainer), CPUX-UR (User Requirements Engineering) und CPUX-UT (Usability Testing and Evaluation, Trainer).
Python Data Analytics
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn.This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.WHAT YOU'LL LEARN* Understand the core concepts of data analysis and the Python ecosystem* Go in depth with pandas for reading, writing, and processing data* Use tools and techniques for data visualization and image analysis* Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWHO THIS BOOK IS FORExperienced Python developers who need to learn about Pythonic tools for data analysis Fabio Nelli is an IT Scientific Application Specialist at IRBM Science Park, a private research center in Pomezia, Roma, Italy. He has been a computer consultant for many years at IBM, EDS, Merck Sharp, and Dohme, along with several banks and insurance companies. He has an Organic Chemistry degree and many years of experience in Information technologies and Automation systems applied to Life Sciences (Tech Specialist at Beckman Coulter Italy and Spain). He is currently developing Java applications that interface Oracle databases with scientific instrumentation generating data and web server applications providing analysis of the results to researchers in real time.PYTHON DATA ANALYTICS1. An Introduction to Data Analysis2. Introduction to the Python's World3. The NumPy Library4. The pandas Library-- An Introduction5. pandas: Reading and Writing Data6. pandas in Depth: Data Manipulation7. Data Visualization with matplotlib8. Machine Learning with scikit-learn9. Deep Learning with TensorFlow10. An Example - Meteorological Data11. Embedding the JavaScript D3 Library in IPython Notebook12. Recognizing Handwritten Digits13. Textual data Analysis with NLTK14. Image Analysis and Computer Vision with OpenCVAppendix AAppendix B
Composable Enterprise: agil, flexibel, innovativ
Der Nutzen der Digitalisierung liegt nicht im Einsatz neuer Technologien für bestehende Prozesse, sondern in organisatorischen Änderungen und neuen Geschäftsmodellen. Das Buch stellt das Composable Enterprise als Leitbild für eine erfolgreiche digitale Transformation und damit verbundene Kostenreduktionen und Umsatzsteigerungen heraus. Was bedeutet das? Ein Composable Enterprise ist dezentral prozessorientiert organisiert. Dadurch kann das Unternehmen schnell auf neue Situationen reagieren, Prozesse und Geschäftsmodelle entwickeln oder verändern. Die Informationssysteme basieren auf Plattformarchitekturen. Ein Paradigmenwechsel zu monolithischen Anwendungen.Branchenkonzepte für Industrie, Consulting und Hochschulen zeigen, wie Organisation und Anwendungsarchitekturen im Composable Enterprise ineinandergreifen.Der Leser erhält Inspiration, Fundament und einen Kompass für die digitale Transformation eines Unternehmens zum Composable Enterprise.PROF. DR. DR. H.C. MULT. AUGUST-WILHELM SCHEER ist einer der prägendsten Wissenschaftler und Unternehmer der deutschen Informationstechnik. Seine Bücher zur Wirtschaftsinformatik sind Standardwerke und in mehrere Sprachen übersetzt. Die von ihm entwickelte Methode ARIS zur Geschäftsprozessmodellierung und -optimierung wird von unübersehbar vielen Unternehmen international eingesetzt. Scheer hat mehrere erfolgreiche Unternehmen gegründet, darunter die IDS Scheer AG, die er vom Start-up zum börsennotierten internationalen Player entwickelt hat. Heute steuert er mit Unternehmen wie Scheer GmbH, imc AG sowie Scheer PAS ein Netzwerk von IT-Unternehmen mit rund 1.300 Mitarbeitern sowie das gemeinnützige August-Wilhelm Scheer Institut. Scheer erhielt für seine Forschungs- und unternehmerischen Leistungen zahlreiche Ehrungen. Einführung - Erfolgstreiber digitaler Geschäftsmodelle - Digitale Branchenkonzepte -Geschäftsprozesse als zentraler Fokus der Digitalisierung - Vom Prozessmodell zum Anwendungssystem - Process Mining - Operational Performance Support - Robotic Process Automation (RPA) - Einfluss der IT-Infrastruktur auf die Prozessautomation - Innovationsnetzwerk zur Digitalisierung
Beginning AWS Security
Improve cloud security within your organization by leveraging AWS’s Shared Responsibility Model, Well-Architected Framework, and the Cloud Adoption Framework. This book will show you to use these tools to make the best decisions for securing your cloud environment.You’ll start by understanding why security is important in the cloud and then review the relevant services offered to meet an organization’s needs. You’ll then move on to the finer points of building a secure architecture and take a deep look into the differences of responsibility of managed services and those that allow customers more control.With multiple AWS services available, organizations must weigh the tradeoffs between those that provide granular control (IaaS), a managed service (PaaS), delivering applications remotely over the internet instead of locally on machines (SaaS). This book will help you to identify the appropriate resources and show you how to implement them to meet an organization’s business, technical, and security perspective in the Cloud Adoption Framework. Finally, you'll see how organizations can launch a secure and optimized cloud architecture and use monitoring tools to be proactive in security measures.With Beginning AWS Security, you'll understand frameworks, models, and the services needed to build a secure architecture.You will:* Review the similarities and differences between cloud and traditional computing.See how security changes when using on-site, hybrid, and cloud models* Develop an understanding that security is not “one and done” * Reinforce the need for updates and monitoring as a continued part of AWS securityWHO THIS BOOK IS FORCloud computing architects, security professionals, security engineers, and software professionals interested in Cloud security.Tasha Penwell is an AWS Educator, AWS Authorized Instructor, solutions architect, and community builder with a focus on security. In her career, she served as the computer science program manager for a community college in Ohio. Tasha has trained professionals on AWS, web development and data analytics. She is the founder and educator of Bytes and Bits, an organization that provides computer science education in Ohio and West Virginia. She is an active presenter and hosts computer science workshops on subjects like cloud computing at high schools around the country. Chapter 1: Why Do I Care About Security? Isn’t that AWS’s problem?Chapter Goal: Identify why security is important in the cloud.No of pages: 40 -50 pagesSub -Topics1. Introduce some real life security breaches and outcomes that have happened in the cloud.2. Describe how AWS provides resources to build a cloud architecture but it’s important to understand the tradeoffs of each service.3. Introduce the Shared Responsibility Model (covered more in Chapter 2)4. Introduce the Well-Architected Framework (will be used as reference throughout the book)5. Describe the similarities and differences between cloud and traditional computing.Chapter 2: Who is Responsible Again?Chapter Goal: Develop an understanding of the Shared Responsibility Model and the tradeoffs of responsibilities based on services used.No of pages: 40 -50Sub - Topics1. Detailed overview of the Shared Responsibility Model2. Elaborate what is meant by “tradeoffs” and why understanding this is important.3. Review of AWS’s security precautions4. Align how the Well-Architected Framework supports the Shared Responsibility Model5. Describe the purpose and responsibilities for Identity and access managementChapter 3: How Do I Build a Secure Architecture?Chapter Goal: Dive deeper into the differences of responsibility of managed services and those that allow customers more control. Identify tradeoffs on specific categories.No of pages : 40 - 50Sub - Topics:1. Identify and understand services, responsibilities, and tradeoffs for computing services.2. Identify and understand services, responsibilities, and tradeoffs for storage services.3. Identify and understand services, responsibilities and tradeoffs for networking services.4. Identify and understand services, responsibilities and tradeoffs for database services.6. Identify and understand services to protect data at rest and in transit.7. Identify and understand services to monitor access and notifications.Chapter 4: Security is Not Built in a DayChapter Goal: Develop an understanding that security is not “one and done” and that updates and monitoring is a continued part of AWS security.No of pages: 40 - 50Sub - Topics:1. Identify and describe what it means to be proactive and reactive in security.2. Identify and implement monitoring services into architecture3. Identify and understand the costs of the monitoring services4. Identify how to make updates and patches to software - and who is responsible for what.Chapter 5: Is This the End?Chapter Goal: Reinforce the need for lifelong learning. Just as security is not a “one and done”, learning should be continuous as well.No of pages: 10 - 20Sub - Topics:1. Identify resources available to continue learning from AWS (AWS Educate, AWS Academy, AWS Skillbuilder)2. Identify resources available to continue learning from the publisher3. A final review of the Shared Responsibility Model.4. A final review of the Well-Architected Framework
Explainable Machine Learning Models and Architectures
EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURESTHIS CUTTING-EDGE NEW VOLUME COVERS THE HARDWARE ARCHITECTURE IMPLEMENTATION, THE SOFTWARE IMPLEMENTATION APPROACH, AND THE EFFICIENT HARDWARE OF MACHINE LEARNING APPLICATIONS.Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. SUMAN LATA TRIPATHI, PHD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener. MUFTI MAHMUD, PHD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.
Solutions Architecture
Explore the complex world of digital solutions architecture and its pivotal role in the modern, technology-driven organization. The book provides a detailed roadmap, charting the intricate processes that solutions architects undertake to transform high-level business propositions into practical, actionable digital solutions.Offering a number of real-world examples, you'll work through examples of various digital projects encompassing cutting-edge technologies such as AI/ML, IoT, Cloud, and the integration with legacy systems. The book also explains how various elements coalesce to form a resilient solutions architecture, shedding light on the need for collaborative synergy between different organizational stakeholders, teams and disciplines.Solutions Architecture underscores the significance of aligning business and technology, demonstrating how this strategic collaboration maximizes the success of digital projects, setting you on the path to a more robust and successful digital future for your organization.WHAT YOU WILL LEARN* Understand the solutions architecture process, including key concepts and best practices* Identify business needs and requirements and translate them into actionable steps that result in effective digital solutions* Select appropriate technologies, build prototypes, and conduct testing and validation* Design, implement, and maintain solutions WHO THIS BOOK IS FORDigital Transformation Program Management, Program Managers, Solutions Architects, IT/Digital Project ManagersAs a digital and technology professional with years of experience in consulting and client organizations, Wasim Rajput has led multiple digital transformation projects and PMOs, and directed teams to deliver enterprise solutions. He has extensive program management and process improvement experience. He has worked with technologies such as 5G, IoT, cloud technologies, AI/ML, and analytics, and has extensive research and writing experience in the area of digital and information technologies.
Knowledge Science - Grundlagen
Knowledge Science beschäftigt sich mit Konzepten, Methoden und Prozessen zur systematischen Erzeugung, Extraktion, Speicherung und Bereitstellung von Wissen zur Lösung von Problemen und lässt sich somit dem Wissensmanagement zuordnen. Kognitive Assistenten sorgen dafür, das richtige Wissen zur richtigen Zeit in der richtigen Art und Weise seinen Anwendern und Anwenderinnen bereitzustellen. Damit dies gelingen kann, kommen inzwischen zahlreiche Methoden der Künstlichen Intelligenz (KI) zur Unterstützung unterschiedlicher Aufgaben des Wissensmanagements zum Einsatz.CARSTEN LANQUILLON beantwortet seit mehr als 25 Jahren unternehmerische Fragestellungen erfolgreich mit Hilfe maschineller Lernverfahren. Er ist seit 2008 Professor an der Hochschule Heilbronn und forscht im Bereich ressourceneffizienter KI in industriellen Anwendungen sowie im Bereich der Sprachtechnologien und kognitiven Assistenzsysteme zur Unterstützung der Wissensarbeit.SIGURD SCHACHT beschäftigt sich seit mehr als 15 Jahren in Theorie und Praxis mit betriebswirtschaftlichen Datenanalysen. Er ist seit 2012 Professor und forscht auf dem Gebiet der Sprachtechnologie und kognitiven Assistenzsysteme mit Schwerpunkt auf dem Trainieren und Anwenden großer Sprachmodelle sowie dem Prompt-Engineering. Einleitung.- Künstliche Intelligenz - Ein Überblick.- Machine Learning.- Deep Learning.- Informationsextraktion aus Texten.- Wissensrepräsentationen.
Zero Trust and Third-Party Risk
DRAMATICALLY LOWER THE CYBER RISK POSED BY THIRD-PARTY SOFTWARE AND VENDORS IN YOUR ORGANIZATIONIn Zero Trust and Third-Party Risk, veteran cybersecurity leader Gregory Rasner delivers an accessible and authoritative walkthrough of the fundamentals and finer points of the zero trust philosophy and its application to the mitigation of third-party cyber risk. In this book, you’ll explore how to build a zero trust program and nurture it to maturity. You will also learn how and why zero trust is so effective in reducing third-party cybersecurity risk. The author uses the story of a fictional organization—KC Enterprises—to illustrate the real-world application of zero trust principles. He takes you through a full zero trust implementation cycle, from initial breach to cybersecurity program maintenance and upkeep. You’ll also find:* Explanations of the processes, controls, and programs that make up the zero trust doctrine* Descriptions of the five pillars of implementing zero trust with third-party vendors* Numerous examples, use-cases, and stories that highlight the real-world utility of zero trustAn essential resource for board members, executives, managers, and other business leaders, Zero Trust and Third-Party Risk will also earn a place on the bookshelves of technical and cybersecurity practitioners, as well as compliance professionals seeking effective strategies to dramatically lower cyber risk. GREGORY C. RASNER is the author of the previous book Cybersecurity & Third-Party Risk: Third-Party Threat Hunting and the content creator of training and certification program “Third-Party Cyber Risk Assessor” (Third Party Risk Association, 2023). Greg is the co-chair for ISC2 Third-Party Risk Task Force and is an advisor to local colleges on technology and cybersecurity.
Practical Next.js for E-Commerce
Leverage the power of Next.js to quickly produce efficient e-commerce sites. This project-oriented book will simplify the process of setting up a starter e-commerce site using Next.js from start to finish, creating a usable e-commerce offer with little more than a text editor or free software. It will equip you with a starting toolset you can use to develop future projects, incorporate into your existing workflow, and help you to take your websites to the next level, reducing reliance on tools that are bloated, prone to being hacked, and not the most efficient.Practical Next.js for E-Commerce is an excellent resource for getting started creating and manipulating e-commerce sites using a static site generator approach. It takes the view that you don’t have to create something complex and unwieldy; you can build something quickly, then extend it using the power of the API or plugins over time, without sacrificing speed or features.WHAT YOU WILL LEARN* Implement e-commerce sites using Next.js* Explore some of the options for architecting an e-commerce site using this framework* Work through a project from start to finish, understanding what can be achieved using Next.js, and where other tools may need to be brought into play WHO THIS BOOK IS FORWeb developers and designers who are interested in learning how to implement the Next.js framework in an e-commerce capacity. ALEX LIBBY is a front-end web developer and seasoned computer book author from England. His passion for all things open source dates back to the days of his degree studies when he first came across web development and he has been hooked ever since. When he isn’t busy developing new features or fixing website bugs for his day job, Alex enjoys tinkering with different open-source libraries to see how they work. He has spent a stint maintaining the jQuery Tools library and enjoys writing about open source technologies, principally for front-end UI development. PART 1:IN THE BEGINNING.- Chapter1 - Getting Started.- PART 2:BUILDING OUR SHOP.- Chapter2 – Laying the Foundations.- Chapter 3 – Adding Products.- Chapter 4 – Checking Out.- Chapter 5 – Styling the Store.- PART 3: WRAPPING THINGS UP.- Chapter 6 – Adapting for Devices.- Chapter 7 – Finessing the Site.- Chapter 8 – Implementing SEO.- Chapter 9 – Testing the Site.- Chapter 10 - Deployment into Production.- PART 4:TAKING THINGS FURTHER.- Chapter 11 – Internationalizing the Site.- Chapter 12 – Adding Authentication.
Causal Artificial Intelligence
DISCOVER THE NEXT MAJOR REVOLUTION IN DATA SCIENCE AND AI AND HOW IT APPLIES TO YOUR ORGANIZATIONIn Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book offers:* Clear and compelling descriptions of the concept of causality and how it can benefit your organization* Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems* Useful strategies for deciding when to use correlation-based approaches and when to use causal inferenceAn enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research. JUDITH S. HURWITZ is the chief evangelist at Geminos Software, a causal AI platform company. For more than 35 years she has been a strategist, technology consultant to software providers, and a thought leader having authored 10 books in topics ranging from augmented intelligence, data analytics, and cloud computing. JOHN K. THOMPSON is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is responsible for the global AI function at EY. He has previously led the global Artificial Intelligence and Rapid Data Lab teams at CSL Behring and is the bestselling author of three books on data analytics.
Causal Artificial Intelligence
DISCOVER THE NEXT MAJOR REVOLUTION IN DATA SCIENCE AND AI AND HOW IT APPLIES TO YOUR ORGANIZATIONIn Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book offers:* Clear and compelling descriptions of the concept of causality and how it can benefit your organization* Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems* Useful strategies for deciding when to use correlation-based approaches and when to use causal inferenceAn enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research. JUDITH S. HURWITZ is the chief evangelist at Geminos Software, a causal AI platform company. For more than 35 years she has been a strategist, technology consultant to software providers, and a thought leader having authored 10 books in topics ranging from augmented intelligence, data analytics, and cloud computing. JOHN K. THOMPSON is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is responsible for the global AI function at EY. He has previously led the global Artificial Intelligence and Rapid Data Lab teams at CSL Behring and is the bestselling author of three books on data analytics.
Teamarbeit im Griff
Viele Dokumente und Arbeitsmappen werden in Teamarbeit erstellt und weitergeführt. Mitarbeitende sollen ihren Anteil einpflegen können, ohne dabei wichtige Inhalte oder das einheitliche Aussehen zu beeinträchtigen. Nacharbeit soll dabei minimiert werden oder am besten ganz unnötig sein. Word und Excel bringen diverse Werkzeuge mit, um die Dateien entsprechend zu schützen. Noch einmal mehr Möglichkeiten zur Teamarbeit bieten Cloud-Speicherorte wie OneDrive und SharePoint. In diesem Heft werden alle Möglichkeiten erklärt, die gemeinsame Arbeit so entspannt wie möglich zu gestalten.Ina Koys ist langjährige Trainerin für MS-Office-Produkte. Viele Fragen werden in den Kursen immer wieder gestellt, aber selten in Fachbüchern behandelt. Einige davon beantwortet sie jetzt in der Reihe "kurz & knackig".
Artificial Intelligence for Sustainable Applications
ARTIFICAL INTELLIGENCE FOR SUSTAINABLE APPLICATIONSTHE OBJECTIVE OF THIS BOOK IS TO LEVERAGE THE SIGNIFICANCE OF ARTIFICIAL INTELLIGENCE IN ACHIEVING SUSTAINABLE SOLUTIONS USING INTERDISCIPLINARY RESEARCH THROUGH INNOVATIVE IDEAS.With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore. This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results. AUDIENCEAI researchers as well as engineers in information technology and computer science. K. UMAMAHESWARI, PHD, is a professor and head with 27 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. B. VINOTH KUMAR, PHD, is an associate professor with 19 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. S. K. SOMASUNDARAM, PHD, is an assistant professor in the Department of Information Technology, PSG College of Technology, Coimbatore, India.
Das Geheimnis hinter ChatGPT
Wie die KI arbeitet und wie sie funktioniert, von Stephen Wolfram, Erstauflage August 2023.Niemand hat damit gerechnet – nicht einmal die Entwickler selbst. ChatGPT hat sich als KI entpuppt, die in der Lage ist, überzeugend auf menschlichem Niveau zu schreiben. Aber wie funktioniert das genau? Was geht in dem »Verstand« dieser KI vor?Der bekannte Wissenschaftler und Pionier Stephen Wolfram liefert in diesem Buch eine lesenswerte und fesselnde Erläuterung der Funktionsweise von ChatGPT, die aus seiner jahrzehntelangen Erfahrung in der IT-Forschung schöpft. Mit anschaulichen Schaubildern und realen Beispielen bietet er einen Blick hinter die Kulissen des beliebten Chatbots. Dabei gibt er nicht nur einen leicht verständlichen Einblick in die Arbeitsweise und das Training neuronaler Netze, sondern zeigt auch detailliert, wie die Sprachverarbeitung von ChatGPT funktioniert und welche Rolle Syntax und Semantik der menschlichen Sprache dabei spielen.Finden Sie heraus, wie ChatGPT die modernste Technologie neuronaler Netze mit grundlegenden Fragen bezüglich der Sprache und des menschlichen Denkens vereint und wie inhaltlich falsche Ausgaben unter Zuhilfenahme von Wolfram|Alpha vermieden werden können.Leseprobe & Inhalt (PDF-Link)Über den Autor:Stephen Wolfram ist preisgekrönter Wissenschaftler und Bestseller-Autor sowie der Schöpfer der weltweit angesehenen Software-Systeme Mathematica, Wolfram|Alpha und Wolfram Language. Seit mehr als 35 Jahren ist er CEO des weltweit agierenden Technologie-Unternehmens Wolfram Reserach. Darüber hinaus ist er für eine Reihe von bahnbrechenden Fortschritten in der Grundlagenwissenschaft verantwortlich, darunter auch sein neuestes Projekt Wolfram Physics Project.
Web API Development for the Absolute Beginner
If you are a developer who wants to learn the basic skills of web and application programming interfaces (APIs) with .NET, this book is your complete introduction. The book takes a learn-by-experience approach. You will hit the ground running with a sample project that has everything you need to be wired up.As you follow along, you will learn simple and intuitive conventions that will free you from some of the more tedious decisions and work, in order to allow you to focus on the business requirements required by your team. Certain components of the framework should always appear in certain folders in the solution to speed up development while others need a name that follows particular conventions. You will learn the building blocks of Web API and how to leverage them to have a well-rounded API. Understanding these small but important tricks will make development faster, easier, and more pleasant, and will prevent time-consuming errors.Part I introduces you to the basics of Web. Part II gets you started creating an API that you will use and build upon throughout the book until you have a complete project. All companion code is available via GitHub. Part III covers more advanced concepts, including how to override out-of-the-box conventions to customize an API to meet your specific business needs. By the end of the book you will have a fully functional API, and you will be better prepared for an interview for a .NET backend developer job.WHAT YOU WILL LEARN* Build a start-to-finish Web API* Know the main concepts of the Web* Apply best practices in API development to your own projects* Know the fundamentals of Web API development* Know the fundamentals of a RESTful API* Leverage Web API constructs to implement a clean and extensible API* Get hands-on experience to unit test a Web API* Gain the skills required to apply for a junior or entry-level .NET Web developer jobWHO THIS BOOK IS FORDevelopers who want to learn API development with .NET. It is helpful to have some basic C# programming knowledge because it is used in API development in .NET, but it is not mandatory. Readers should be familiar with a programming language to be able to understand code and examples. Experience with web development is not necessary.IRINA DOMINTE is an independent consultant and trainer, international speaker, software architect, Microsoft MVP for Developer Technologies, and a Microsoft Certified Trainer (MCT) with a wealth of experience. Having taught classes, workshops, and presentations for over 2,000 hours, Irina is passionate about coding and keeping abreast of the latest trends and best practices in software architecture and .NET.Twice a year, for five months, Irina teaches .NET and C# to aspiring software developers, people interested in software development or seeking to expand their knowledge. She is an active member of the community and has founded the DotNet Iasi User Group and the dotnetdays.ro conference, where she connects with like-minded developers who are eager to share their expertise and insights.Irina is also a prolific blogger, and her website, https://irina.codes, features a wealth of articles on various coding topics. She decided to write this book after learning first-hand how much people struggle to grasp new concepts without proper guidance.Part 1 - API basics* Web API1.1. What is web api1.2. When to use Web API1.3. The world of web applications1.4. Summary* Introduction to Web2.1. HTTP Protocol2.2. Request2.3. Response2.4. Headers2.5. HTTP Verbs2.6. Summary* Setting up the environment3.1. Installing Visual Studio3.2. Installing PostMan3.3. Your first Web Api Project3.4. Issuing your first request3.5. Summary* Web Api - Building blocks4.1. Convention over Configuration4.2. Controllers & Actions4.3. Models vs DTO4.4. Routing4.5. Model Binding4.6. Middlewares4.7. Dependency Injection4.8. SummaryPart 2 - Implementing an API* Getting started5.1. Your first RESTful API5.2. What is REST5.3. Implementing Get5.4. Implementing POST5.5. Implementing HEAD5.6. Implementing PUT5.7. Implementing DELETE5.8. Summary* Introducing an ORM6.1. Introducing Entity Framework6.2. Connecting to a database6.3. Adding migrations6.4. Summary* Getting organized7.1. Splitting code into layers7.2. Data Layer, implementing a repository7.3. Domain Layer7.3.1. Extending a domain object7.4. Services Layer7.5. Introducing AutoMapper7.6. Wire everything in the controller7.7. Summary* Routing8.1. What is a route8.2. How to customize routing8.3. Attribute routing8.4. Adding two different controllers under the same route8.5. Route constraints8.6. Summary* Middlewares9.1. Introducing custom middlewares9.2. Middleware usage scenarios9.3. Create your own middleware to add some headers9.4. SummaryPart 3 - Advanced API Topics* Model Binding 10.1. Create your own Model Binder10.2. Register your ModelBinder10.3. Value Providers10.4. When to use a value provider10.5. Model Validation10.6. Summary* Versioning The API11.1. Ways of versioning11.2. Versioning in URL path11.3. Versioning in QueryString11.4. Versioning in Headers11.5. General rules about versioning11.6. Deprecating Endpoints11.7. Summary* Documenting The API12.1. Introducing Open API12.2. Working with Swashbuckle* Testing The API13.1. What is a test13.2. Writing unit tests13.3. Writing integrations test13.4. Summary
Explainable Machine Learning Models and Architectures
EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURESTHIS CUTTING-EDGE NEW VOLUME COVERS THE HARDWARE ARCHITECTURE IMPLEMENTATION, THE SOFTWARE IMPLEMENTATION APPROACH, AND THE EFFICIENT HARDWARE OF MACHINE LEARNING APPLICATIONS.Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. SUMAN LATA TRIPATHI, PHD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener. MUFTI MAHMUD, PHD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees. Preface xiiiAcknowledgements xv1 A COMPREHENSIVE REVIEW OF VARIOUS MACHINE LEARNING TECHNIQUES 1Pooja Pathak and Parul Choudhary1.1 Introduction 11.1.1 Random Forest 21.1.2 Decision Tree 31.1.3 Support Vector Machine 41.1.4 Naive Bayes 51.1.5 K-Means Clustering 61.1.6 Principal Component Analysis 61.1.7 Linear Regression 61.1.8 Logistic Regression 71.1.9 Semi-Supervised Learning 81.1.10 Transductive SVM 91.1.11 Generative Models 91.1.12 Self-Training 91.1.13 Relearning 91.2 Conclusions 92 ARTIFICIAL INTELLIGENCE AND IMAGE RECOGNITION ALGORITHMS 11Siddharth, Anuranjana and Sanmukh Kaur2.1 Introduction 122.2 Traditional Image Recognition Algorithms 132.2.1 Harris Corner Detector (1988) 132.2.2 SIFT (2004) 152.2.3 ASIFT 162.2.4 SURF (2006) 172.3 Neural Network-Based Algorithms 212.4 Convolutional Neural Network Architecture 222.5 Various CNN Architectures 232.5.1 LeNet-5 (1998) 232.5.2 AlexNet (2012) 242.5.3 VGGNet (2014) 242.5.4 GoogleNet (2015) 243 EFFICIENT ARCHITECTURES AND TRADE-OFFS FOR FPGA-BASED REAL-TIME SYSTEMS 31L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem3.1 Overview of FPGA-Based Real-Time System 313.1.1 Key Elements of Real-Time System 323.1.2 Real-Time System and its Computation 323.1.3 FPGA Functionality and Applications 333.1.4 FPGA Applications 333.1.5 FPGA Architecture 343.1.6 Reconfigurable Architectures 353.2 Hybrid FPGA Configurations and its Algorithms 383.2.1 Hybrid FPGA 383.2.2 Hybrid FPGA Architecture 393.2.3 Hybrid FPGA Configuration 403.3 Hybrid FPGA Algorithms 423.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 443.4 CNN Hardware Accelerator Architecture Overview 463.5 Summary 474 A LOW-POWER AUDIO PROCESSING USING MACHINE LEARNING MODULE ON FPGA AND APPLICATIONS 49Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud4.1 Introduction 494.2 Existing Machine Learning Modules and Audio Classifiers 504.3 Audio Processing Module Using Machine Learning 564.4 Application of Proposed FPGA-Based ML Models 574.5 Implementation of a Microphone on FPGA 594.6 Conclusion 604.7 Future Scope 605 SYNTHESIS AND TIME ANALYSIS OF FPGA-BASED DIT-FFT MODULE FOR EFFICIENT VLSI SIGNAL PROCESSING APPLICATIONS 65Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda5.1 Introduction 665.2 Implementation of DIT-FFT Algorithm 675.2.1 A Quick Overview of DIT-FFT 675.2.2 Algorithmic Representation with Example 695.2.3 Simulated Output Waveform 695.3 Synthesis of Designed Circuit 715.4 Static Timing Analysis of Designed Circuit 735.5 Result and Discussion 775.6 Conclusion 776 ARTIFICIAL INTELLIGENCE-BASED ACTIVE VIRTUAL VOICE ASSISTANT 81Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash6.1 Introduction 826.2 Literature Survey 836.3 System Functions 876.4 Model Training 886.5 Discussion 906.5.1 Furnishing Movie Recommendations 916.5.2 KNN Algorithm Book Recommendation 926.6 Results 936.7 Conclusion 1027 IMAGE FORGERY DETECTION: AN APPROACH WITH MACHINE LEARNING 105Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das7.1 Introduction 1057.2 Historical Background 1077.3 CNN Architecture 1097.4 Analysis of Error Level of Image 1137.5 Proposed Model of Image Forgery Detection, Results and Discussion 1157.6 Conclusion 1187.7 Future Research Directions 1198 APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN OPTICAL PERFORMANCE MONITORING 123Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam8.1 Introduction 1238.2 Algorithms Employed for Performance Monitoring 1298.2.1 Artificial Neural Networks 1298.2.2 Deep Neural Networks 1308.2.3 Convolutional Neural Networks 1318.2.3.1 Convolutional Layer 1318.2.3.2 Non-Linear Layer 1328.2.3.3 Pooling Layer 1328.2.3.4 Fully Connected Layer 1328.2.4 Support Vector Regression (SVR) 1338.2.5 Support Vector Machine (SVM) 1338.2.6 Kernel Ridge Regression (KRR) 1338.2.7 Long Short-Term Memory (LSTM) 1338.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 1348.3.1 Performance Monitoring 1348.3.2 Applications of AI in Optical Networking 1358.4 Optical Impairments and Fault Management 1358.4.1 Noise 1358.4.2 Distortion 1358.4.3 Timing 1368.4.4 Component Faults 1368.4.5 Transmission Impairments 1378.4.6 Fault Management in Optical Network 1378.5 Conclusion 1389 WEBSITE DEVELOPMENT WITH DJANGO WEB FRAMEWORK 141Sanmukh Kaur, Anuranjana and Yashasvi Roy9.1 Introduction 1419.2 Salient Features of Django 1429.2.1 Complete 1429.2.2 Versatile 1429.2.3 Secure 1429.2.4 Scalable 1439.2.5 Maintainable 1439.2.6 Portable 1439.3 UI Design 1439.3.1 HTML 1439.3.2 CSS 1449.3.3 Bootstrap 1449.4 Methodology 1449.5 UI Design 1449.6 Backend Development 1489.6.1 Login Page 1489.6.2 Registration Page 1499.6.3 User Tracking 1499.7 Ouputs 1509.8 Conclusion 15210 REVENUE FORECASTING USING MACHINE LEARNING MODELS 155Yashasvi Roy and Sanmukh Kaur10.1 Introduction 15510.2 Types of Forecasting 15610.2.1 Qualitative Forecasting 15610.2.1.1 Industries That Use Qualitative Forecasting 15710.2.1.2 Qualitative Forecasting Methods 15810.2.2 Quantitative Forecasting 15810.2.2.1 Quantitative Forecasting Methods 15910.2.3 Artificial Intelligence Forecasting 16010.2.3.1 Artificial Neural Network (ANN) 16010.2.3.2 Support Vector Machine (SVM) 16110.3 Types of ML Models Used in Finance 16210.3.1 Linear Regression 16210.3.1.1 Simple Linear Regression 16210.3.1.2 Multiple Linear Regression 16210.3.2 Ridge Regression 16310.3.3 Decision Tree 16410.3.3.1 Prediction of Continuous Variables 16410.3.3.2 Prediction of Categorical Variables 16510.3.4 Random Forest Regressor 16510.3.5 Gradient Boosting Regression 16610.3.5.1 Advantages of Gradient Boosting 16710.4 Model Performance 16710.4.1 R-Squared Method 16710.4.2 Mean Squared Error (MSE) 16710.4.3 Root Mean Square Error (RMSE) 16810.5 Conclusion 16811 APPLICATION OF MACHINE LEARNING OPTIMIZATION TECHNIQUES IN WIND RESOURCE ASSESSMENT 171Udhayakumar K. and Krishnamoorthy R.11.1 Introduction 17211.2 Wind Data Analysis Methods 17311.2.1 Wind Characteristics Parameters 17311.2.2 Wind Speed Distribution Methods 17311.2.3 Weibull Method 17411.2.4 Goodness of Fit 17511.3 Wind Site and Measurement Details 17511.3.1 Seasonal Wind Periods 17611.3.2 Machine Learning and Optimization Techniques 17611.3.2.1 Moth Flame Optimization (MFO) Method 17611.4 Results and Discussions 18011.4.1 Wind Characteristics 18211.4.1.1 Kayathar Station (Onshore) 18211.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 18711.4.1.3 Jafrabad (Gujarat-Nearshore) 19211.4.2 Wind Distribution Fitting 19511.4.2.1 Kayathar Station (Onshore) 19611.4.2.2 Bimodal Behaviour 19611.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 20211.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 20311.4.3 Optimization Methods for Parameter Estimation 21211.4.3.1 Optimization Parameters Comparison 21211.4.4 Wind Power Density Analysis (WPD) 21411.4.4.1 Comparison of Wind Power Density 21511.5 Research Summary 22111.6 Conclusions 22212 IOT TO SCALE-UP SMART INFRASTRUCTURE IN INDIAN CITIES: A NEW PARADIGM 227Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala12.1 Introduction 22812.2 Technological Progress: A Brief History 22912.3 What is the Internet of Things (IoT)? 23012.4 Economic Effects of Internet of Things 23012.5 Infrastructure and Smart Infrastructure: The Difference 23212.5.1 What is Smart Infrastructure? 23312.5.2 What are the Principles of Smart Infrastructure? 23412.5.3 Components of IoT-Based Smart City Project 23512.6 Architecture for Smart Cities 23612.6.1 Networking Technologies 23712.6.2 Network Topologies 23712.6.3 Network Architectures 23812.6.3.1 Home Area Networks (HANs) 23812.6.3.2 Field/Neighborhood Area Networks (FANs/NANs) 23812.6.3.3 Wide Area Networks (WANs) 23812.6.3.4 Network Protocols 23812.7 IoT Technology in India’s Smart Cities: The Current Scenario 23912.8 Challenges in IoT-Based Smart City Projects 24312.8.1 Technological Challenges 24312.8.1.1 Privacy and Security 24312.8.1.2 Smart Sensors and Infrastructure Essentials 24312.8.1.3 Networking in IoT Systems 24412.8.1.4 Big Data Analytics 24412.8.2 Financial - Economic Challenges 24412.9 Role of Explainable AI 24512.10 Conclusion and Future Scope 246References 246Index 251
Einsatzmöglichkeiten von GPT in Finance, Compliance und Audit
Dieses Buch wurde von drei Experten geschrieben, die mit Unterstützung von GPT, einen umfassenden Einblick in Einsatzmöglichkeiten generativer AI, wie GPT, im Finanzbereich von Unternehmen geben. Es liefert Hintergründe, Vorteile sowie die Herausforderungen und Risiken bei der Implementierung. Es werden neben allgemeinen Anwendungsmöglichkeiten im Unternehmen ebenso spezifische Anwendungen dargestellt. Dabei wird konkret in den Bereichen Controlling, Business Intelligence, Accounting, Investor Relationship, Innenrevision und Kontrollsysteme, Risikomanagement, Wirtschaftsprüfung und Datenschutz eingegangen. Das Buch befasst sich abschließend mit der Strategie im Einsatz von GPT.Hintergrund und Vorteile von GPT.- Herausforderungen und Risiken im Einsatz von GPT.- Allgemeine Anwendungsmöglichkeiten im Unternehmen.- Einsatzbeispiele in Controlling, Business Intelligence, Accounting, Investor Relationship, Innenrevision und Kontrollsysteme, Risikomanagement, Wirtschaftsprüfung und Datenschutz.- Strategie im Einsatz von GPT.
Building Responsible AI Algorithms
This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.WHAT YOU WILL LEARN* Build AI/ML models using Responsible AI frameworks and processes* Document information on your datasets and improve data quality* Measure fairness metrics in ML models* Identify harms and risks per task and run safety evaluations on ML models* Create transparent AI/ML models* Develop Responsible AI principles and organizational guidelinesWHO THIS BOOK IS FORAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms TOJU DUKE is a Responsible AI Program Manager at Google with over 17 years of experience spanning across advertising, retail, not-for-profits, and tech industries. She designs Responsible AI programs focused on the development and implementation of Responsible AI frameworks, processes, and tools across Google’s product and research teams. Toju is also the Founder of Diverse in AI, a community interest organization with a mission to provide inclusive and diverse AI through humanity. She provides consultation and advice on Responsible AI practices to organizations worldwide.IntroductionPART I. FOUNDATION1. Responsibility2. AI Principles3. DataPART II. IMPLEMENTATION4. Responsible AI Framework5. Fairness6. Safety7. Humans in the Loop8. Transparency9. Privacy and RobustnessPART III. ETHICAL CONSIDERATIONS10. Ethics of AI and MLReferences
KI 2.023
Die Künstliche Intelligenz beschäftigt immer häufiger die Medien und damit die Menschen. Doch was steckt dahinter? Ist die Künstliche Intelligenz der Heilsbringer für eine phantastische Zukunft, in der sich die Menschen frei entfalten können und befreit sind von Routinetätigkeiten?Oder ist Künstliche Intelligenz eine Bedrohung für die Menschheit?Dieses Buch will Antworten auf diese Fragen geben. Auf Basis von Fakten, ausgehend vom aktuellen Stand der Forschung. Und basierend auf den Forschungsergebnissen wird ein Trend aufgezeichnet, wie sich die Künstliche Intelligenz entwickeln wird und damit auch die Menschliche Gesellschaft.Und zeigt damit die Chancen und Risiken auf in der Hoffnung, dass jeder für sich einen Überblick erhält, um mit dem Thema verantwortungsvoll umzugehen.Uwe Irmer:Dipl. Ing. Uwe Irmer studierte Elektrotechnik und Wirtschaftsingenieur mit den Schwerpunkten Energietechnik, Informatik und Projektmanagement. Seit 1990 in vielen nationalen und internationalen Projekten der Fachbereiche Energieverteilung, Informationssicherheit und IT Architektur tätig, befasst er sich seit 1992 mit dem Thema Informationssicherheit und forscht auf den Gebieten Projektmanagement, Informationssicherheit, IT Forensics, Künstliche Intelligenz und Cloud Technologie.
Artificial Intelligence for Sustainable Applications
ARTIFICAL INTELLIGENCE FOR SUSTAINABLE APPLICATIONSTHE OBJECTIVE OF THIS BOOK IS TO LEVERAGE THE SIGNIFICANCE OF ARTIFICIAL INTELLIGENCE IN ACHIEVING SUSTAINABLE SOLUTIONS USING INTERDISCIPLINARY RESEARCH THROUGH INNOVATIVE IDEAS.With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore. This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results. AUDIENCEAI researchers as well as engineers in information technology and computer science. K. UMAMAHESWARI, PHD, is a professor and head with 27 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. B. VINOTH KUMAR, PHD, is an associate professor with 19 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. S. K. SOMASUNDARAM, PHD, is an assistant professor in the Department of Information Technology, PSG College of Technology, Coimbatore, India.
Exploring the Power of ChatGPT
Learn how to use the large-scale natural language processing model developed by OpenAI: ChatGPT. This book explains how ChatGPT uses machine learning to autonomously generate text based on user input and explores the significant implications for human communication and interaction.Author Eric Sarrion examines various aspects of ChatGPT, including its internal workings, use in computer projects, and impact on employment and society. He also addresses long-term perspectives for ChatGPT, including possible future advancements, adoption challenges, and considerations for ethical and responsible use. The book starts with an introduction to ChatGPT covering its versions, application areas, how it works with neural networks, NLP, and its advantages and limitations. Next, you'll be introduced to applications and training development projects using ChatGPT, as well as best practices for it. You'll then explore the ethical implications of ChatGPT, such as potential biases and risks, regulations, and standards. This is followed by a discussion of future prospects for ChatGPT. The book concludes with practical use case examples, such as text content creation, software programming, and innovation and creativity.This essential book summarizes what may be one of the most significant developments in artificial intelligence in recent history and provides useful insights for researchers, policymakers, and anyone interested in the future of technology.WHAT YOU WILL LEARN* Understand the basics of deep learning and text generation using language models such as ChatGPT* Prepare data and train a language model to generate text* Use ChatGPT for various applications such as marketing text generation or answering questions* Understand the use of ChatGPT through the OpenAI API and how to optimize model performanceWHO THIS BOOK IS FORSoftware developers and professionals, researchers, students, and people interested in learning more about this field and the future of technology.ERIC SARRION is a trainer, developer, and independent consultant. He has been involved in all kinds of IT projects over the past 30 years. He is also a long-time author of web development technologies and is renowned for the clarity of his explanations and examples. He resides in Paris, France.Part 1: Introduction to ChatGPT1 - What is ChatGPT ?Describes hat is ChatGPT, its history...• 1.1 Definition of ChatGPT• 1.2 ChatGPT History• 1.3 Versions of ChatGPT• 1.4 Application areas of ChatGPT2 - How Does ChatGPT Work?Describes how it works inside• 2.1 Neural networks• 2.2 Natural language processing techniques used by ChatGPT• 2.3 The data used to train ChatGPT• 2.4 The advantages and limitations of ChatGPT3 - Applications of ChatGPTDescribes what you can do whith ChatGPT• 3.1 Chatbots and virtual assistants• 3.2 Machine translation apps• 3.3 Content writing apps• 3.4 Applications in information retrievalPart 2: How To Train and Use ChatGPT4 - ChatGPT TrainingDescribes how to build the models used by ChatGPT• 4.1 Data collection and preparation• 4.2 ChatGPT training settings• 4.3 Training tools available• 4.4 Techniques to improve ChatGPT performance5 - Using ChatGPT in Development ProjectsDescribes how to use ChatGPT in a web page with an API• 5.1 Libraries and frameworks for ChatGPT• 5.2 Examples of projects using ChatGPT• 5.3 Techniques to integrate ChatGPT into applications• 5.4 Use ChatGPT with the OpenAI API• 5.5 Use ChatGPT with a voice interface• 5.6 Methods to evaluate the performance of ChatGPT6 - Best Practices for Using ChatGPTDescribes how to optimize ChatGPT• 6.1 Strategies to ensure the quality of input data• 6.2 Techniques to avoid bias in data• 6.3 Methods to optimize ChatGPT performance• 6.4 ChatGPT maintenance tipsPart 3 The Ethical Implications of ChatGPT7 - Potential Biases and Risks of ChatGPTDescribes biases and riks of ChatGPT• 7.1 Sources of bias in the data• 7.2 The risks of discrimination and stigmatization• 7.3 The limits of ChatGPT transparency• 7.4 Consequences for privacy and data security8 - The Implications of ChatGPT on Employment and SocietyDescribes impacts on employment and society• 8.1 The impacts on employment in various sectors• 8.2 The implications for education and vocational training• 8.3 Consequences for social and cultural norms• 8.4 Political and legal responses to the changes brought about by ChatGPT9 - Regulations and Standards for Using ChatGPTDescribes responsible use of ChatGPT• 9.1 Existing regulations for consumer protection• 9.2 Standards for Responsible Use of ChatGPT• 9.3 ChatGPT governance initiatives• 9.4 Considerations for Legal and Ethical Responsibility of ChatGPTPart 4 Future Prospects of ChatGPT10 - Future Developments of ChatGPTDescribes future developments• 10.1 Advances in Machine Learning and Natural Language Processing Research• 10.2 ChatGPT performance and efficiency improvements• 10.3 Advances in applications and areas of use of ChatGPT• 10.4 Developments in the competition and the ChatGPT market11 - The Long Term Outlook for ChatGPTDescribes long term outlook• 11.1 The implications for artificial intelligence and cognition• 11.2 Merging possibilities between ChatGPT and other emerging technologies• 11.3 The challenges of adopting and accepting ChatGPT• 11.4 Issues for regulation and governance of ChatGPTPart 5 : Examples of Using ChatGPT12 - Using ChatGPT for Text Content Creation13 - Using ChatGPT for Software Programming14 - Using ChatGPT for Text Translation15 - Using ChatGPT for Artistic Content Creation16 - Using ChatGPT for Innovation and Creativity17 - ConclusionGives a conclusion of the book• 17.1 Summaries of the key elements covered in the book• 17.2 Final thoughts on the impact and implications of ChatGPT• 17.3 Suggestions for future research and development on ChatGPT• 17.4 Considerations for the ethical and responsible use of ChatGPT in the future.• 17.5 In conclusion
Pro Power BI Theme Creation
Use JSON theme files to standardize the look of Power BI dashboards and reports. This book shows how you can create theme files using the Power BI Desktop application to define high-level formatting attributes for dashboards as well as how to tailor detailed formatting specifications for individual dashboard elements in JSON files. Standardize the look of your dashboards and apply formatting consistently over all your reports. The techniques in this book provide you with tight control over the presentation of all aspects of the Power BI dashboards and reports that you create.Power BI theme files use JSON (JavaScript Object Notation) as their structure, so the book includes a brief introduction to JSON as well as how it applies to Power BI themes. The book further includes a complete reference to all the current formatting definitions and JSON structures that are at your disposal for creating JSON theme files up to the May 2023 release of Power BI Desktop. Finally, the book includes dozens of theme files, from the simple to the most complex, that you can adopt and adapt to suit your own requirements.WHAT YOU WILL LEARN* Produce designer output without manually formatting every individual visual in a Power BI dashboard* Standardize presentation for families of dashboard types* Switch presentation styles in a couple of clicks* Save dozens, or hundreds, of hours laboriously formatting dashboards* Define enterprise-wide presentation standards* Retroactively apply standard styles to existing dashboardsWHO THIS BOOK IS FORPower BI users who want to save time by defining standardized formatting for their dashboards and reports, IT professionals who want to create corporate standards of dashboard presentation, and marketing and communication specialists who want to set organizational standards for dashboard delivery.ADAM ASPIN is an independent business intelligence consultant based in the United Kingdom. He has worked with SQL Server for over 25 years, and now focuses on Power BI. During this time, he has developed several dozen BI and analytics systems based on the Microsoft BI product suite. Adam has been creating JSON theme files since the feature was first introduced in Power BI Desktop, and has delivered corporate Power BI themes for dozens of clients across Europe.Adam is a graduate of Oxford University. He has applied his skills for a range of clients in finance, banking, utilities, telecoms, construction, and retail. He is the author of a number of Apress books: Pro Power BI Dashboard Creation; Pro DAX and Data Modeling in Power BI; Pro Data Mashup in Power BI; SQL Server Data Integration Recipes; Business Intelligence with SQL Server Reporting Services; High Impact Data Visualization in Excel with Power View, 3D Maps, Get and Transform and Power BI; and Data Mashup with Microsoft Excel Using Power Query and M.1. Introduction to Power BI Themes2. Create and Customize a Theme In Power BI Desktop3. High-Level Theme Definition4. Default Visual Styles5. Object Visual Styles6. Card and Table Visual Styles7. Classic Chart Visual Styles8. Complex Chart Visual Styles9. Other Chart Visual Styles10. Maps11. Miscellaneous Visual Styles12. Dashboard Styling13. Cascading Styles