Computer und IT
CompTIA A+ Complete Study Guide, 2-Volume Set
YOUR COMPLETE, ACCURATE RESOURCE FOR THE UPDATED COMPTIA A+ CORE 1 AND CORE 2 EXAMSIn the newly revised sixth edition of CompTIA A+ Complete Study Guide 2-Volume Set: Volume 1 Core 1 Exam 220-1201 and Volume 2 Core 2 Exam 220-1202, you'll discover comprehensive coverage of all A+ certification exam objectives. A team of A+ certified IT professionals with a combined 50 years' experience in the industry walk you through the most popular information technology certification on the market today, preparing you for success on both the 220-1201 and 220-1202 A+ exams. The set emphasizes on-the-job skills you'll use every day as a PC technician or in a related role, with timely updates covering major advances in mobile, cloud, network, and security technology. It walks you through mobile devices, networking, hardware, virtualization and cloud computing, hardware and network troubleshooting, operating systems, security, software troubleshooting, and operational procedures. You'll also find:* Practical examples and technology insights drawn from the real-world experiences of current IT professionals* Exam highlights, end-of-chapter reviews, and other useful features that help you learn and retain the detailed info contained within* Complimentary access to the Sybex online test bank, including hundreds of practice test questions, flashcards, and a searchable key term glossaryPrepare smarter and faster, the Sybex way. CompTIA A+ Complete Study Guide 2-Volume Set is perfect for anyone preparing to take the A+ certification exams for the first time, as well as those seeking to renew their A+ certification and PC or hardware technicians interested in upgrading their skillset.
NoOps
Traditional DevOps is struggling with new challenges in today's fast-changing software world. With the rise of microservices, cloud-based systems, and AI-driven automation, managing software has become increasingly difficult. Teams often deal with too many tools, repetitive manual tasks, and slow innovation. NoOps provides a clear guide to using AI to streamline DevOps and reduce manual work.The book starts by explaining how DevOps has evolved and why software development has become so fragmented. It highlights the importance of standardization as the first step toward NoOps. Readers will learn how AI can improve coding, testing, infrastructure management, and software deployment. It covers AI-powered development tools, automated testing, self-managing infrastructure, and intelligent AI agents that handle deployments and fix problems automatically. Real-world case studies show how companies are already using AI to transform their DevOps processes. Beyond automation, NoOps also explores how AI will change job roles, requiring new skills and shifting how teams work. It discusses ethical concerns, team dynamics, and the future of AI-driven software development.Whether you're a developer, DevOps engineer, or tech leader, this book will help you understand and prepare for a future where AI plays a major role in software delivery.What you will learn:* How DevOps has evolved and why traditional methods struggle with modern software challenges.* How AI can automate coding, testing, and infrastructure management to streamline workflows.* Explore AI-driven DevOps strategies, including AI orchestration, self-healing infrastructure, and predictive analytics.* Discover real-world case studies of companies successfully using AI to improve software delivery.Who this book is for:Technical Executives, DevOps Engineers & SREs looking to automate testing, monitoring, infrastructure, and CI/CD. Software Developers who want to write better code faster using AI-driven development tools. QA Engineers & Testers responsible for functional, integration, and performance testing who need to automate and self-heal test cases with AI.Chapter 1 – The Evolution of DevOps.- Chapter 2 - Fragmented Software Development: Why DevOps Isn’t Always Enough.- Chapter 3 – The Case for Standardization: Building the Foundation for NoOps.- Chapter 4 - Cloud-Native & Data-Centric Approaches.- Chapter 5 – What “Good” Looks Like.- Chapter 6 – Generative AI for Coding & Unit Testing.- Chapter 7 – Generative AI for System & Integration Testing.- Chapter 8 – Generative AI for IaC & Data Provisioning.- Chapter 9 – AI-Orchestration.- Chapter 10 – Rise of AI Agents.- Chapter 11 – Rethinking Roles in NoOps World.- Chapter 12 – The Future: Beyond DevOps to NoOps.
3D-Druck mit Resin für Einsteiger
Resindruck verstehen und sicher anwendenResindruck gilt als präzise, aber anspruchsvoll. Der Autor erläutert alle dafür benötigten Grundlagen und Techniken leicht verständlich – egal, ob Sie kompletter Neueinsteiger oder Umsteiger vom FDM-Druck sind. Sie finden eine fundierte Einführung in Technik, Materialwahl, Sicherheitsaspekte und Vorbereitung und werden Schritt für Schritt vom Aufbau Ihres Druckers bis zum ersten erfolgreichen Druck begleitet.Von der Idee zum perfekten DruckSie erfahren, wie Sie geeignete 3D-Modelle finden oder selbst erstellen und optimal für den Resindruck vorbereiten. Anhand eines durchgängigen Beispielprojekts – dem Druck eines filigranen Drachenmodells – lernen Sie den gesamten Workflow kennen: slicen, Stützstrukturen setzen, drucken, waschen, aushärten und nachbearbeiten.Tipps und Anleitungen aus der PraxisAnschauliche Anleitungen und zahlreiche Praxistipps erleichtern den Einstieg und helfen, typische Fehler zu vermeiden. Der klare Aufbau und die langjährige Erfahrung des Autors machen dieses Buch zu einem hilfreichen Begleiter – ideal für alle, die sich mit Freude und Sicherheit an den Resindruck wagen möchten.Über den Autor:Stephan Knaus beschäftigt sich seit 2015 leidenschaftlich mit dem Thema 3D-Druck und kennt die typischen Herausforderungen beim Drucken mit Resin aus erster Hand. Als Betreiber der Community drucktipps3d.de verfasst er Testberichte, Tutorials und bietet täglich fundierte Unterstützung im Forum.
A Friendly Guide to Data Science
UNLOCK THE WORLD OF DATA SCIENCE—NO CODING REQUIRED.Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics—what data analysis involves, which skills are useful, and how terms like “data analytics” and “machine learning” connect—without getting too technical too fast.Data science isn’t just about crunching numbers, pulling data from a database, or running fancy algorithms. It’s about asking the right questions, understanding the process from start to finish, and knowing what’s possible (and what’s not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security—because working with data means thinking about people, too.Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today’s most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts— just as AI and data become increasingly central to everyday life.WHAT YOU WILL LEARN* Grasp foundational statistics and how it matters in data analysis and data science* Understand the data science project life cycle and how to manage a data science project* Examine the ethics of working with data and its use in data analysis and data science* Understand the foundations of data security and privacy* Collect, store, prepare, visualize, and present data* Identify the many types of machine learning and know how to gauge performance* Prepare for and find a career in data scienceWHO THIS BOOK IS FORA wide range of readers who are curious about data science and eager to build a strong foundation. Perfect for undergraduates in the early semesters of their data science degrees, as it assumes no prior programming or industry experience. Professionals will find particular value in the real-world insights shared through practitioner interviews. Business leaders can use it to better understand what data science can do for them and how their teams are applying it. And for career changers, this book offers a welcoming entry point into the field—helping them explore the landscape before committing to more intensive learning paths like degrees or boot camps.Part I: Foundations.- Chapter 1: Working with Numbers: What Is Data, Really?.- Chapter 2: Figuring Out What’s Going on in the Data: Descriptive Statistics.- Chapter 3: Setting Us Up for Success: The Inferential Statistics Framework and Experiments.- Chapter 4: Coming to Complex Conclusions: Inferential Statistics and Statistical Testing.- Chapter 5: Figuring Stuff Out: Data Analysis.- Chapter 6: Bringing It into the 21st Century: Data Science.- Chapter 7: A Fresh Perspective: The New Data Analytics.- Chapter 8: Keeping Everyone Safe: Data Security and Privacy.- Chapter 9: What’s Fair and Right: Ethical Considerations.- Part II: Doing Data Science.- Chapter 10: Grasping the Big Picture: Domain Knowledge.- Chapter 11: Tools of the Trade: Python and R.- Chapter 12: Trying Not to Make a Mess: Data Collection and Storage.- Chapter 13: For the Preppers: Data Gathering and Preprocessing.- Chapter 14: Ready for the Main Event: Feature Engineering, Selection, and Reduction.- Chapter 15: Not a Crystal Ball: Machine Learning.- Chapter 16: How’d We Do? Measuring the Performance of ML Techniques.- Chapter 17: Making the Computer Literate: Text and Speech Processing.- Chapter 18: A New Kind of Storytelling: Data Visualization and Presentation.- Chapter 19: This Ain’t Our First Rodeo: ML Applications.- Chapter 20: When Size Matters: Scalability and the Cloud.- Chapter 21: Putting It All Together: Data Science Solution Management.- Chapter 22: Errors in Judgment: Biases, Fallacies, and Paradoxes.- Part III: The Future.- Chapter 23: Getting Your Hands Dirty: How to Get Involved in Data Science.- Chapter 24: Learning and Growing: Expanding Your Skillset and Knowledge.- Chapter 25: Is It Your Future?: Pursuing a Career in Data Science.- Appendix A.
Artificial Intelligence in Neurological Disorders
THE BOOK GIVES INVALUABLE INSIGHTS INTO HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING THE MANAGEMENT AND TREATMENT OF NEUROLOGICAL DISORDERS, EMPOWERING YOU TO STAY AHEAD IN THE RAPIDLY EVOLVING LANDSCAPE OF HEALTHCARE.Embark on a groundbreaking exploration of the intersection between cutting-edge technology and the intricate complexities of neurological disorders. Artificial Intelligence in Neurological Disorders: Management, Diagnosis and Treatment comprehensively introduces how artificial intelligence is becoming a vital ally in neurology, offering unprecedented advancements in management, diagnosis, and treatment. As the digital age converges with medical expertise, this book unveils a comprehensive roadmap for leveraging artificial intelligence to revolutionize neurological healthcare. Delve into the core principles that underpin AI applications in the field by exploring intricate algorithms that enhance the precision of diagnosis and how machine learning not only refines the understanding of neurological disorders but also paves the way for personalized treatment strategies tailored to individual patient needs. With compelling case studies and real-world examples, the realms of neuroscience and artificial intelligence converge, illustrating the symbiotic relationship that holds the promise of transforming patient care. Readers of this book will find it:* Provides future perspectives on advancing artificial intelligence applications in neurological disorders;* Focuses on the role of AI in diagnostics, delving into how advanced algorithms and machine learning techniques contribute to more accurate and timely diagnosis of neurological disorders;* Emphasizes practical integration of AI tools into clinical practice, offering insights into how healthcare professionals can leverage AI technology for more effective patient care;* Recognizes the interdisciplinary nature of neurology and AI, bridging the gap between these fields, making it accessible to healthcare professionals, researchers, and technologists;* Addresses the ethical implications of AI in healthcare, exploring issues such as data privacy, bias, and the responsible deployment of AI technologies in the neurological domain.AUDIENCEResearchers, scientists, industrialists, faculty members, healthcare professionals, hospital management, biomedical industrialists, engineers, and IT professionals interested in studying the intersection of AI and neurology. RISHABHA MALVIYA, PHD is an associate professor in the Department of Pharmacy in the School of Medical and Allied Services at Galgotias University with over 13 years of research experience. He has authored 57 books, 58 chapters, and over 150 research papers for national and international journals of repute, as well as 51 patents. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. SURAJ KUMAR is an assistant professor in the School of Medical and Allied Sciences at Galgotias University. He has published over ten papers in international journals and five book chapters. His research interests include sustainable polymeric fibers, nanoparticles, and controlled drug delivery. ADITYA SUSHIL SOLANKE, PHD is a Senior Resident in Neurosurgery at Byramjee Jeejeebhoy Government Medical College and Sassoon Hospital, India. He completed his Bachelor of Medicine, Bachelor of Surgery, and Masters in General Surgery from the Government Medical College in Nagpur. PRIYANSHI GOYAL, M.PHARM is an assistant professor in the School of Pharmacy at Mangalayatan University. She has authored seven review articles and two books and attended 14 national and international conferences and webinars. Her area of interest is treatment strategies for neurological disorders. KAPIL CHAUHAN, PHD is an emergency physician at Max Hospital in Dehradun, India. He completed his Bachelor of Medicine and Bachelor of Surgery from Teerthanker Mahavir Medical College and Masters in Emergency Medicine from Max Hospital.
Einstieg in die Datenanalyse mit Excel
In diesem Praxisbuch führt George Mount Daten- und Business-Analyst*innen durch zwei leistungsstarke in Excel integrierte Tools für die Datenanalyse: Power Pivot und Power Query. Er zeigt Ihnen, wie Sie mit Power Query Workflows zur Datenbereinigung erstellen und mit Power Pivot relationale Datenmodelle entwerfen. Darüber hinaus lernen Sie nützliche Funktionen wie dynamische Array-Formeln, KI-gestützte Methoden zur Aufdeckung von Trends und Mustern sowie die Integration von Python zur Automatisierung von Analysen und Berichten kennen.George Mount zeigt Ihnen, wie Sie Ihre Excel-Kenntnisse weiter ausbauen und das Programm effektiv für die moderne Datenanalyse nutzen. Das Buch gibt einen klaren Überblick über das volle Potenzial von Excel und hilft Ihnen, auch ohne Power BI oder anderer Zusatzsoftware Daten effektiv aufzubereiten und zu analysieren. Viele Übungen zur Vertiefung des Gelernten runden das Buch ab.Dieses Buch zeigt Ihnen, wie Sie:wiederholbare Datenbereinigungsprozesse für Excel mit Power Query erstellenrelationale Datenmodelle und Analysen mit Power Pivot entwickelnDaten schnell mit dynamischen Arrays abrufenKI nutzen, um Muster und Trends in Excel aufzudeckenPython-Funktionen in Excel für automatisierte Analysen und Berichte integrierenLeseprobe (pdf-Link)Über den Autor:George Mount ist Gründer von Stringfest Analytics, einer Beratungsfirma, die sich auf die berufliche Weiterbildung im Bereich Datenanalyse spezialisiert hat. Er hat mit führenden Bootcamps, Lernplattformen und Fachorganisationen zusammengearbeitet, um Menschen dabei zu unterstützen, in der Datenanalyse erfolgreich zu sein. George Mount wurde mit dem „Microsoft Most Valuable Professional“-Award für herausragende technische Expertise und sein Engagement in der Excel-Community ausgezeichnet.
Large Language Models selbst programmieren
LLMs selbst erstellen und von Grund auf verstehen!Der Bestseller aus den USA ist jetzt endlich in deutscher Übersetzung erhältlich und bietet den idealen Einstieg in die Welt der Large Language Models (LLMs). Auf dem eigenen Laptop entwickeln, trainieren und optimieren Sie ein LLM, das mit GPT-2 vergleichbar ist – ganz ohne vorgefertigte Bibliotheken. Bestsellerautor Sebastian Raschka erklärt die Grundlagen und Vorgehensweise Schritt für Schritt und leicht verständlich.Das Buch nimmt Sie mit auf eine spannende Reise in die Blackbox der generativen KI: Sie programmieren ein LLM-Basismodell, bauen daraus einen Textklassifikator und schließlich einen eigenen Chatbot, den Sie als persönlichen KI-Assistenten nutzen können. Dabei lernen Sie nicht nur die technischen Details kennen, sondern auch, wie Sie Datensätze vorbereiten, Modelle mit eigenen Daten verfeinern und mithilfe von menschlichem Feedback verbessern. Ein praxisnaher Leitfaden für alle, die wirklich verstehen wollen, wie LLMs funktionieren – weil sie ihren eigenen gebaut haben.Über den Autor:Sebastian Raschka, PhD, arbeitet sehr mehr als einem Jahrzehnt im Bereich Machine Learning und KI. Er ist Staff Research Engineer bei Lightning AI, wo er LLM-Forschung betreibt und Open-Source-Software entwickelt. Sebastian ist nicht nur Forscher, sondern hat auch eine große Leidenschaft für die Vermittlung von Wissen. Bekannt ist er für seine Bestseller zu Machine Learning mit Python und seine Beiträge zu Open Source.Richtet sich an:Data ScientistsPython-Programmierer*innen, die sich tiefer in die Funktionsweise von LLMs einarbeiten möchten und ML-Grundkenntnisse haben
Einstieg in die Datenanalyse mit Excel
Daten analysieren und Prozesse automatisieren mit Excel - Automatisieren Sie Ihre Datenbereinigung in Excel mit Power Query. - Strukturieren Sie Ihre Daten mit Power Pivot. - Nutzen Sie Python für automatisierte Analysen und Berichterstattung. In diesem Praxisbuch führt George Mount Daten- und Business-Analyst*innen durch zwei leistungsstarke in Excel integrierte Tools für die Datenanalyse: Power Pivot und Power Query. Er zeigt Ihnen, wie Sie mit Power Query Workflows zur Datenbereinigung erstellen und mit Power Pivot relationale Datenmodelle entwerfen. Darüber hinaus lernen Sie nützliche Funktionen wie dynamische Array-Formeln, KI-gestützte Methoden zur Aufdeckung von Trends und Mustern sowie die Integration von Python zur Automatisierung von Analysen und Berichten kennen. George Mount zeigt Ihnen, wie Sie Ihre Excel-Kenntnisse weiter ausbauen und das Programm effektiv für die moderne Datenanalyse nutzen. Das Buch gibt einen klaren Überblick über das volle Potenzial von Excel und hilft Ihnen, auch ohne Power BI oder anderer Zusatzsoftware Daten effektiv aufzubereiten und zu analysieren. Viele Übungen zur Vertiefung des Gelernten runden das Buch ab. Dieses Buch zeigt Ihnen, wie Sie: - wiederholbare Datenbereinigungsprozesse für Excel mit Power Query erstellen - relationale Datenmodelle und Analysen mit Power Pivot entwickeln - Daten schnell mit dynamischen Arrays abrufen - KI nutzen, um Muster und Trends in Excel aufzudecken - Python-Funktionen in Excel für automatisierte Analysen und Berichte integrieren
Generative KI im Kontext der Wirtschaftsinformatik
Generative KI stellt eine neue wichtige Technologie dar, die in der Lage ist, betriebliche Prozesse und Anwendungen zu automatisieren und zu optimieren. Inwieweit sich Generative KI wie ChatGPT für den Einsatz im Kontext der Wirtschaftsinformatik eignet, wird im Rahmen dieses Herausgeber-Bandes thematisiert. Dazu untersuchen einige Autoren-Teams den Einsatz von ChatGPT anhand verschiedener Beispiele. Die Beispiele umfassen sowohl Fragestellungen zum Lernen an der Hochschule wie zum Beispiel zur Texterstellung und zur Mathematik als auch zum betrieblichen Einsatz in der unternehmerischen Praxis in verschiedenen betriebswirtschaftlichen Anwendungsfeldern. Das Buch bietet wichtige Informationen, die für Praktiker ebenso relevant sind wie für Studierende und Lehrende..- Teil 1 – Einleitung..- 1 Generative KI im Kontext digitaler Ressourcen, Lehre, Innovation und Kundenbeziehung..- Teil 2 – Digitale Ressourcen und Lehre..- 2 Versionsverwaltung von Dokumenten in Zeiten von Cloud, Cybersecurity und KI..- 3 "Versteht" ein System wie ChatGPT seine eigenen Texte zur Mathematik?..- 4 Ein Vergleich von Wirtschaftsinformatik-Lehrmaterialien mit generierten Erklärungstexten..- Teil 3 – Innovationsmanagement..- 5 Die Zukunft des Technologiescoutings: Wie ein digitales Transfertool von KI-basierter Contentgenerierung profitiert..- 6 Zukunftsszenarien Szenarioanalysen mit ChatGPT – Potenziale und Grenzen KI-gestützter Vorausschau..- Teil 4 – Kundenanalyse und Kundeninteraktion..- 7 Data Science und KI – made by Data Science und KI..- 8 Wie Produktivitätsgewinne in der Kundenbetreuung durch KI-basierte Textgenerierung erzielt werden – heutige und zukünftige Einsatzmöglichkeiten..- 9 Multimodales Fenster in die Vergangenheit der ehemaligen Vauban-Festung Saarlouis mittels ChatGPT.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Maschinelles Lernen lernen
Maschinelles Lernen prägt zunehmend unseren Alltag, doch fehlt bislang eine Fachdidaktik, die das Thema als Lerngegenstand für Schulen und Hochschulen systematisch untersucht. In der vorliegenden Arbeit werden Methoden der fachdidaktische Entwicklungsforschung genutzt, um unter Einbezug verschiedener Naturwissenschaftsdidaktiken eine Lehr-Lernumgebung zu maschinellem Lernen für Ingenieurstudierende zu entwickeln. Die qualitative und quantitative empirische Untersuchung des entwickelten Materials in Design-Experimenten liefert Einsichten in die Lernprozesse der Studierenden, um den didaktischen Diskurs zu bereichern und Hinweise für die Lehrpraxis abzuleiten.Einleitung.- Maschinelles Lernen.- Spezifizierung und Strukturierung der Lerngegenstände.- Design(weiter)entwicklung.- Designentwicklung und Datenerhebung.- Methoden der Datenanalyse.- Die Wirksamkeitsanalyse.- Evaluation der Mikrozyklen.- Erstellung und Nutzung von ML-Modellen.- Das individuelle Modellkonzept.- Die Weiterentwicklung des Designs und der Wirksamkeitszyklus.- Zusammenfassung und Diskussion.- Ausblick.
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311
96 Common Challenges in Power Query
This comprehensive guide is designed to address the most frequent and challenging issues faced by users of Power Query, a powerful data transformation tool integrated into Excel, Power BI, and Microsoft Azure. By tackling 96 real-world problems with practical, step-by-step solutions, this book is an essential resource for data analysts, Excel enthusiasts, and Power BI professionals. It aims to enhance your data transformation skills and improve efficiency in handling complex data sets.Structured into 12 chapters, the book covers specific areas of Power Query such as data extraction, referencing, column splitting and merging, sorting and filtering, and pivoting and unpivoting tables. You will learn to combine data from Excel files with varying column names, handle multi-row headers, perform advanced filtering, and manage missing values using techniques such as linear interpolation and K-nearest neighbors (K-NN) imputation. The book also dives into advanced Power Query functions such as Table.Group, List.Accumulate, and List.Generate, explored through practical examples such as calculating running totals and implementing complex grouping and iterative processes. Additionally, it covers crucial topics such as error-handling strategies, custom function creation, and the integration of Python and R with Power Query.In addition to providing explanations on the use of functions and the M language for solving real-world challenges, this book discusses optimization techniques for data cleaning processes and improving computational speed. It also compares the execution time of functions across different patterns and proposes the optimal approach based on these comparisons.In today’s data-driven world, mastering Power Query is crucial for accurate and efficient data processing. But as data complexity grows, so do the challenges and pitfalls that users face. This book serves as your guide through the noise and your key to unlocking the full potential of Power Query. You’ll quickly learn to navigate and resolve common issues, enabling you to transform raw data into actionable insights with confidence and precision.WHAT YOU WILL LEARN* Master data extraction and transformation techniques for various Excel file structures* Apply advanced filtering, sorting, and grouping methods to organize and analyze data* Leverage powerful functions such as Table.Group, List.Accumulate, and List.Generate for complex transformations* Optimize queries to execute faster* Create and utilize custom functions to handle iterative processes and advanced list transformation* Implement effective error-handling strategies, including removing erroneous rows and extracting error reasons* Customize Power Query solutions to meet specific business needs and share custom functions across filesWHO THIS BOOK IS FORAspiring and developing data professionals using Power Query in Excel or Power BI who seek practical solutions to enhance their skills and streamline complex data transformation workflowsOMID MOTAMEDISEDEH is a seasoned data analyst, educator, and author with extensive experience in business intelligence and data visualization. Based in Australia, he specializes in leveraging Microsoft tools to provide practical solutions for complex data challenges. Omid is the author of eight books, seven written in Persian and one in English, covering a wide range of topics, including Excel, data visualization, and Power Query. His works are known for their clear explanations and actionable insights, catering to data professionals, students, and educators alike. Holding a PhD in Industrial Engineering, Omid combines academic depth with over a decade of hands-on experience in the IT industry, where he has served as a consultant and manager. His professional journey includes implementing data-driven strategies, optimizing processes, and mentoring teams to achieve their analytical goals. Omid is deeply committed to education and knowledge sharing. Beyond writing, he conducts workshops, creates YouTube tutorials, and mentors aspiring data analysts.Chapter 1: Data Extraction from Files.- Chapter 2: Referencing.- Chapter 3: Sorting & Filtering.- Chapter 4: Column Splitting & Merging.- Chapter 5: Pivoting & Unpivoting Tables.- Chapter 6: Grouping Rows with Table.Group.- Chapter 7: Merging & Appending Tables.- Chapter 8: Handling Missing Values.- Chapter 9: Looping in Power Query.- Chapter 10: Leveraging Scripting and External Integrations in Power Query.- Chapter 11: Error Handling Strategies.- Chapter 12: Custom Functions.
Software Engineering Made Easy
Learn how to write good code for humans. This user-friendly book is a comprehensive guide to writing clear and bug-free code. It integrates established programming principles and outlines expert-driven rules to prevent you from over-complicating your code.You’ll take a practical approach to programming, applicable to any programming language and explore useful advice and concrete examples in a concise and compact form. Sections on Single Responsibility Principle, naming, levels of abstraction, testing, logic (if/else), interfaces, and more, reinforce how to effectively write low-complexity code. While many of the principles addressed in this book are well-established, it offers you a single resource._Software Engineering Made Easy_ modernizes classic software programming principles with quick tips relevant to real-world applications. Most importantly, it is written with a keen awareness of how humans think. The end-result is human-readable code that improves maintenance, collaboration, and debugging—critical for software engineers working together to make purposeful impacts in the world.WHAT YOU WILL LEARN* Understand the essence of software engineering.* Simplify your code using expert techniques across multiple languages.* See how to structure classes.* Manage the complexity of your code by using level abstractions.* Review test functions and explore various types of testing.WHO THIS BOOK IS FORIntermediate programmers who have a basic understanding of coding but are relatively new to the workforce. Applicable to any programming language, but proficiency in C++ or Python is preferred. Advanced programmers may also benefit from learning how to deprogram bad habits and de-complicate their code.MARCO GÄHLER began his career studying physics at ETH Zurich before transitioning to software engineering. In 2018, he joined Zurich Instruments, where he developed electronic devices used in quantum computing. Throughout his career, Marco has observed the pitfalls in code written by self-taught developers, for example PhD students, and recognized the need for clear, practical guidance on simple programming practices. This book reflects his preference for clear, short functions, and minimal class usage, aiming to make good programming practices accessible to all.Chapter 1: Fundamentals of Software Engineering.- Chapter 2: Components of Code.- Chapter 3: Classes.- Chapter 4:Testing.- Chapter 5: Design Principles.- Chapter 6: Programming.- Chapter 7: High-Level Design.- Chapter 8: Refactoring.- Chapter 9: Other Common Topics.- Chapter 10: Collaborating.- Glossary.
Microsoft Power Platform Solution Architect Certification Companion
This comprehensive guide book equips you with the knowledge and confidence needed to prep for the exam and thrive as a Power Platform Solution Architect.The book starts with a foundation for successful solution architecture, emphasizing essential skills such as requirements gathering, governance, and security. You will learn to navigate customer discovery, translate business needs into technical requirements, and design solutions that address both functional and non-functional needs. The second part of the book delves into the Microsoft Power Platform ecosystem, offering an in-depth look at its core components—Power Apps, Power Automate, Power BI, Microsoft Copilot, and Robotic Process Automation (RPA).Detailed insights into data modeling, security strategies, and AI integration will guide you in building scalable, secure solutions. Coverage of application life cycle management, which empowers solution architects to design, implement, and deploy Power Platform solutions effectively, is discussed next. You will then go through real-world scenarios, giving you a practical understanding of the challenges and considerations in managing Power Platform projects within a business context.The book concludes with strategies for continuous learning and resources for professional development, including practice questions to assess knowledge and readiness for the PL-600 exam. After reading the book, you will be ready to take the exam and become a successful Power Platform Solution Architect.WHAT YOU WILL LEARN* Understand the Solution Architect's role, responsibilities, and strategic approaches to successfully navigate projects* Master the basics of Power Platform Solution Architecture* Understand governance, security, and integration concepts in real-world scenarios* Design and deploy effective business solutions using Power Platform components* Gain the skills necessary to prep for the PL-600 certification examWHO THIS BOOK IS FORProfessionals pursuing Microsoft PL-600 Solution Architect certification and IT consultants and developers transitioning to solution architect rolesLOGANATHAN K is a seasoned Microsoft Certified Trainer (MCT) and Functional Consultant with extensive experience in Power Platform, Dynamics 365, and business process automation. Currently working as a Functional Consultant in a Microsoft Partner company, he is passionate about helping organizations drive digital transformation through the Power Platform and Microsoft Business Applications.As the author of the popular blog LK Techs (lktechs.com), he shares knowledge of Microsoft technologies, certification paths, and real-world use cases to help individuals build careers in IT. He holds several advanced certifications, including those in Microsoft Business Central and the Power Platform, and regularly conducts training sessions for students, professionals, and educators.Chapter 1: Getting Started with the PL-600 Exam: Overview and Essentials.- Chapter 2: Building a Successful Solution Architect Framework: Key Stages and Skills.- Chapter 3: Governance, Architecture, and Core Components in Power Platform and Dynamics 365.- Chapter 4: Leveraging Microsoft Copilot, RPA, and Securing Data Models in Power Platform Solutions.- Chapter 5: Implementing Analytics, AI, and ALM Strategies for Power Platform Success.- Chapter 6: Evaluating Your Expertise Through Real-World Scenarios.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311
Navigating Misinformation
Informed navigation of misinformation on social media constitutes a major challenge. The field of Human-Computer Interaction (HCI) suggests digital misinformation interventions as user-centered countermeasures. This book clusters (1) existing misinformation interventions within a taxonomy encompassing designs, interaction types, and timings. The book demonstrates that current research mostly addresses higher-educated participants, and targets Twitter/X and Facebook. It highlights trends toward comprehensible interventions in contrast to top-down approaches. The findings informed (2) the design, implementation, and evaluation of simulated apps for TikTok, voice messages, and Twitter/X as indicator-based interventions. Therefore, (3) the book identified misinformation indicators for various modalities that were perceived as comprehensible.The book empirically demonstrates that (4) indicator-based interventions are positively received due to their transparency. However, they also come with challenges, such as users' blind trust and lack of realistic assessments of biases. This research outlines chances and implications for future research.
Konstruierte Wahrheiten
In einer Welt, in der immer mehr Fake News verbreitet werden, wird es zunehmend schwieriger, Wahrheit und Lüge, Wissen und Meinung auseinanderzuhalten. Desinformationskampagnen werden nicht nur als ein politisches Problem wahrgenommen, vielmehr geht es in der Fake-News-Debatte auch um fundamentale philosophische Fragen: Was ist Wahrheit? Wie können wir sie erkennen? Gibt es so etwas wie objektive Fakten oder ist alles sozial konstruiert? Dieses Buch erklärt, wie Echokammern und alternative Weltbilder entstehen, es macht das postfaktische Denken für die gegenwärtige Wahrheitskrise verantwortlich und zeigt, wie wir einem drohenden Wahrheitsrelativismus entgehen können.THOMAS ZOGLAUER (Dr. phil. habil.) lehrt Philosophie an der Brandenburgischen Technischen Universität Cottbus-Senftenberg und an der Graduierten-Akademie der Universität Stuttgart und ist Autor zahlreicher Bücher zur Technikphilosophie und angewandten Ethik.Filterblasen und Echokammern.- Verschwörungstheorien.- Fake News.- Epistemologie des Postfaktischen.- Wahrheitstheorien.- Information und Wissen.
Protecting and Mitigating Against Cyber Threats
THE BOOK PROVIDES INVALUABLE INSIGHTS INTO THE TRANSFORMATIVE ROLE OF AI AND ML IN SECURITY, OFFERING ESSENTIAL STRATEGIES AND REAL-WORLD APPLICATIONS TO EFFECTIVELY NAVIGATE THE COMPLEX LANDSCAPE OF TODAY’S CYBER THREATS.Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples. Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multi-disciplinary discussion influencing cybersecurity and digital resilience in the future. Readers will find in this book:* Provides comprehensive coverage on various aspects of security solicitations, ranging from theoretical foundations to practical applications;* Includes real-world case studies and examples to illustrate how AI and machine learning technologies are currently utilized in security solicitations;* Explores and discusses emerging trends at the intersection of AI, machine learning, and security solicitations, including topics like threat detection, fraud prevention, risk analysis, and more;* Highlights the growing importance of AI and machine learning in security contexts and discusses the demand for knowledge in this area.AUDIENCECybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity. SACHI NANDAN MOHANTY, PHD is an associate professor at the School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India, He has published 60 articles in journals of international repute, edited 24 books, and serves as an editor for several international journals. His research interests include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, cognition, and computational intelligence. SUNEETA SATPATHY, PHD is an associate professor in the Center for Artificial Intelligence and Machine Learning at Siksha O. Anusandhan University, India. She has published several papers in international journals and conferences of repute and edited numerous books. Her research interests include computer forensics, cyber security, data fusion, data mining, big data analysis, and decision mining. MING YANG, PHD is a professor in the College of Computing and Software Engineering at Kennesaw State University, Georgia, USA and serves as a consultant for many companies. He has published over 70 peer-reviewed conference and journal papers and book chapters in addition to serving as an editor for several journals. His research interests include image processing, multimedia communication, computer vision, and machine learning. D. KHASIM VALI, PHD is an assistant professor in the School of Computer Science and Engineering, the Vellore Institute of Technology, Andhra Pradesh University, India, with over 18 years of teaching experience. He has 21 international publications to his credit and is a life member of ISTE and IETE. His research interests include artificial intelligence, machine learning, and deep learning.
Artificial Intelligence in Neurological Disorders
THE BOOK GIVES INVALUABLE INSIGHTS INTO HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING THE MANAGEMENT AND TREATMENT OF NEUROLOGICAL DISORDERS, EMPOWERING YOU TO STAY AHEAD IN THE RAPIDLY EVOLVING LANDSCAPE OF HEALTHCARE.Embark on a groundbreaking exploration of the intersection between cutting-edge technology and the intricate complexities of neurological disorders. Artificial Intelligence in Neurological Disorders: Management, Diagnosis and Treatment comprehensively introduces how artificial intelligence is becoming a vital ally in neurology, offering unprecedented advancements in management, diagnosis, and treatment. As the digital age converges with medical expertise, this book unveils a comprehensive roadmap for leveraging artificial intelligence to revolutionize neurological healthcare. Delve into the core principles that underpin AI applications in the field by exploring intricate algorithms that enhance the precision of diagnosis and how machine learning not only refines the understanding of neurological disorders but also paves the way for personalized treatment strategies tailored to individual patient needs. With compelling case studies and real-world examples, the realms of neuroscience and artificial intelligence converge, illustrating the symbiotic relationship that holds the promise of transforming patient care. Readers of this book will find it:* Provides future perspectives on advancing artificial intelligence applications in neurological disorders;* Focuses on the role of AI in diagnostics, delving into how advanced algorithms and machine learning techniques contribute to more accurate and timely diagnosis of neurological disorders;* Emphasizes practical integration of AI tools into clinical practice, offering insights into how healthcare professionals can leverage AI technology for more effective patient care;* Recognizes the interdisciplinary nature of neurology and AI, bridging the gap between these fields, making it accessible to healthcare professionals, researchers, and technologists;* Addresses the ethical implications of AI in healthcare, exploring issues such as data privacy, bias, and the responsible deployment of AI technologies in the neurological domain.AUDIENCEResearchers, scientists, industrialists, faculty members, healthcare professionals, hospital management, biomedical industrialists, engineers, and IT professionals interested in studying the intersection of AI and neurology. RISHABHA MALVIYA, PHD is an associate professor in the Department of Pharmacy in the School of Medical and Allied Services at Galgotias University with over 13 years of research experience. He has authored 57 books, 58 chapters, and over 150 research papers for national and international journals of repute, as well as 51 patents. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. SURAJ KUMAR is an assistant professor in the School of Medical and Allied Sciences at Galgotias University. He has published over ten papers in international journals and five book chapters. His research interests include sustainable polymeric fibers, nanoparticles, and controlled drug delivery. ADITYA SUSHIL SOLANKE, PHD is a Senior Resident in Neurosurgery at Byramjee Jeejeebhoy Government Medical College and Sassoon Hospital, India. He completed his Bachelor of Medicine, Bachelor of Surgery, and Masters in General Surgery from the Government Medical College in Nagpur. PRIYANSHI GOYAL, M.PHARM is an assistant professor in the School of Pharmacy at Mangalayatan University. She has authored seven review articles and two books and attended 14 national and international conferences and webinars. Her area of interest is treatment strategies for neurological disorders. KAPIL CHAUHAN, PHD is an emergency physician at Max Hospital in Dehradun, India. He completed his Bachelor of Medicine and Bachelor of Surgery from Teerthanker Mahavir Medical College and Masters in Emergency Medicine from Max Hospital.
Mathematics for Digital Science 3
Over the past century, advancements in computer science have consistently resulted from extensive mathematical work. Even today, innovations in the digital domain continue to be grounded in a strong mathematical foundation. To succeed in this profession, both today's students and tomorrow’s computer engineers need a solid mathematical background.The goal of this book series is to offer a solid foundation of the knowledge essential to working in the digital sector. Across three volumes, it explores fundamental principles, digital information, data analysis, and optimization. Whether the reader is pursuing initial training or looking to deepen their expertise, the Mathematics for Digital Science series revisits familiar concepts, helping them refresh and expand their knowledge while also introducing equally essential, newer topics.GÉRARD-MICHEL COCHARD is Professor Emeritus at Université de Picardie Jules Verne, France, where he has held various senior positions. He has also served at the French Ministry of Education and the CNAM (Conservatoire National des Arts et Métiers). His research is conducted at the Eco-PRocédés, Optimisation et Aide à la Décision (EPROAD) laboratory, France.MHAND HIFI is Professor of Computer Science at Université de Picardie Jules Verne, France, where he heads the EPROAD UR 4669 laboratory and manages the ROD team. As an expert in operations research and NP-hard problem-solving, he actively contributes to numerous international conferences and journals in the field.
The AI Act Handbook
Compliant Usage of Artificial Intelligence in the Private and Public Sectors- Detailed overview of the AI Act- Impact of the AI Act on various areas (including fi nance, employment law, advertising and administration)- Related areas of law (data protection, IP and IT law)- Practical overview of AI governance, risk and compliance in companies- Information on standards, norms and certificationsBy experts for practitioners – with this handbook, you can prepare yourself for the requirements of the European AI Act in a practical and compliant manner. Get comprehensive information on the effects on the various application fields of artificial intelligence in the private and public sectors. After a brief introduction to the history and technology of AI, you will receive a detailed subsumption of the content of the AI Act based on the various risk categories. Subsequently, areas of law closely related to the use of AI, in particular data protection, IP and IT law, will be dealt with in detail. By providing case studies, the book shares insights about the impact of the AI Act on various areas such as autonomous driving, work, critical infrastructure, medicine, insurance, etc. The correlation with the areas of law relevant to these areas will also be considered. A practical overview of the topic of AI governance, risk and compliance (GRC) in companies, tips on the application of guidelines and governance frameworks, implementation ideas for trustworthy AI as well as standards, norms and certifications complement the book.The TEAM OF AUTHORS consists of lawyers specializing in IT and data protection law and the use of AI. It includes, among others, one of Austria's representative in the AI Act negotiations at EU Council level and the founder of the Austrian association Women in AI.FROM THE CONTENTS- What Is AI and How Do Data Science and Data Analytics Differ?- Geopolitics of Artificial Intelligence- AI Act: Rights and Obligations- Data Protection- Intellectual Property- AI and IT Contract Law- Private Sector- Public Sector- Ethics- Governance in the Company