Computer und IT
Architected Intelligence
Transform AI ambition into durable results with Architected Intelligence Architected Intelligence is an actionable guide for leaders responsible for turning AI experimentation into systems that work reliably at scale. Rather than treating AI as a standalone initiative, the book presents a unified framework for designing human-AI systems that align strategy, data, engineering, and organizational execution. Drawing on years of hands-on experience building and operating AI systems in production, Jacob Miller and Jeremy Mumford introduce a set of principles that apply equally to enterprise transformation and individual AI solutions. The framework spans five dimensions that connect strategy, data, models, trust, and enablement into one buildable system. Written for technical executives and practitioners, Architected Intelligence offers a clear path forward for organizations navigating rapid change. It provides the mental models needed to build AI systems that continue to perform as the technology landscape evolves. "Architected Intelligence provides a clear, practical vision for how business leaders and engineers can navigate the AI transformation together. ... This is a great book if you want your mind prepared for What Comes Next." —ABE GONG, Founder at Katabase and Great Expectations; Operating Advisor at Bessemer Venture Partners "I've seen too many AI initiatives die in the last mile between a dazzling demo and a dependable workflow. Architected Intelligence gives me a practical architecture to close that gap. If you want AI that ships and scales, this is the guide I'd hand your team." —JEPSON TAYLOR, Founder and CEO at VEOX; former Chief AI Strategist at Dataiku and DataRobot "We are entering a remarkable new world shaped by AI systems and increasingly autonomous agents, and the organizations that will thrive are those that build on the right foundation from the start. Architected Intelligence gives you exactly that. The authors bring together disciplines that are too often treated in isolation. They ground agents, data, models, and systems design in a coherent and practical framework that any builder or leader can put to work immediately. Having spent decades at the forefront of enterprise technology, I've seen how the right mental model can change everything. This book gives you that model." —SHAOWN NANDI, Director of Technology at AWS Most AI initiatives fail. Transform AI potential into organizational results through the Architected Intelligence framework. The gap between a dazzling "cool demo" and a reliable, production-grade system is a chasm that is swallowing teams and entire organizations. Architected Intelligence is your definitive map at both the organizational and tactical levels to cross this divide. Drawing on their experience building the world's largest ecommerce accelerator, authors Jacob Miller and Jeremy Mumford deliver actionable guidance for organizations struggling to turn proofs of concept into production systems. Whether you are competing in the age of AI or looking to establish technology leadership in your sector, this book organizes AI success around a unified framework of five core components: Design AI Systems to Deliver Impactful OutputPower AI with High-Quality Input Data Engineer, Optimize, and Integrate AI ModelsCreate Trust through ObservabilityScale Transformation through AI Enablement The book also provides: Practical Roadmaps for Both AI Products and AI Automation: The book provides concrete implementation roadmaps for two of the most critical AI use cases: AI as product features and AI for process automation. Readers leave equipped to avoid the all-or-nothing trap through incremental development and to build systems that perform even as the technology landscape rapidly evolves. A Toolkit for Trust, Unstructured Data Governance, and Evaluation: Readers learn how to disaggregate trust into its underlying elements, establish comprehensive and feasible unstructured data governance for organizations of any size, and apply a full suite of evaluation methods to determine whether AI systems are genuinely performing. Built for Technical Executives and the Engineers Who Implement,Architected Intelligence is perfect for technical CEOs, CTOs, product managers, leaders in data science, directors of engineering, and anyone responsible for execution seeking to understand the wider vision. If you want to lead out on AI, this foundational reference will equip you with the mental models and practical tools needed to build AI systems that ship, scale, and succeed. JACOB MILLER is the Vice President of Data Science at Pattern, the world's largest e-commerce accelerator. He has assisted organizations and leaders implementing AI solutions with demonstrable and immediate benefits. JEREMY MUMFORD is the Lead AI Engineer at Pattern. He brings together data and software engineering with formal training in databases and data science to build production-ready generative AI systems.
Driving Digital Transformation with Microsoft Foundry
In today’s rapidly evolving digital landscape, businesses must move beyond traditional strategies to unlock the transformative potential of AI. Driving Digital Transformation with Azure AI Foundry is a hands-on guide to leading this change using Microsoft’s powerful cloud and AI ecosystem. From aligning business goals with AI capabilities and designing user-centric solutions to harnessing real-time data and enabling intelligent automation, this book covers the entire AI lifecycle using Azure’s powerful suite—Azure AI Foundry, OpenAI, Cognitive Services, Synapse, Fabric, and more. It starts with an introduction to Azure AI Foundry and setting up its environment, followed by design and building of AI solutions. With an emphasis on responsible AI, security, and scalability, the book explores key principles for governance, trust, and ethical innovation in Azure AI Foundry. Real-world case studies across industries—healthcare, retail, finance, manufacturing, and government—demonstrate Azure AI Foundry’s tangible impact. Whether you're a business leader, technologist, or architect, this book equips you with the knowledge to unlock AI's full potential with Azure AI Foundry. After reading the book, you will learn to recognize high-impact use cases, align business objectives with Azure AI Foundry capabilities, and understand the essential building blocks of a modern, digital-first organization. What You Will Learn: Build & Launch Impactful AI Solutions on AzureShape an AI-Powered & Responsible OrganizationDrive Tangible Business Outcomes & Scale SuccessLeverage powerful Azure services like Azure Machine Learning, Azure OpenAI Service, and Cognitive Services within their organization's projects. Who This Book Is For: Azure AI Architects and Data Professionals
The Spark Of AI
When Neuronimo, a curious natural-born neuron, meets Electra, a brilliant artificial one, a journey of discovery begins. Together, they explore the world of Artificial Intelligence. How machines learn, adapt, reason, and even dream. Through vivid storytelling, playful metaphors, and real concepts made simple, this book invites curious minds of all ages to grasp how neural networks work, and why understanding them matters. It's not just a tale of code and circuits; it's a conversation between two sparks, lighting the path to responsible, powerful, and human-centered AI.
Cybersicherheit für Dummies
Schützen Sie Ihre privaten oder geschäftlichen Daten! Dieses Buch führt Sie in die Grundlagen der Cybersicherheit ein. Sie erfahren, welche Bedrohungen es gibt, wie Sie sie erkennen und wie Sie sich vor ihnen schützen. Joseph Steinberg zeigt Ihnen, was Sie unbedingt tun sollten, um sich zu schützen, und wie Sie sicher von zu Hause oder unterwegs arbeiten. Und falls Sie dann doch von einem Angriff betroffen sind, lernen Sie, wie Sie Ihre Daten wiederherstellen. Das Buch hilft Ihnen, Schwachstellen in Ihren Systemen zu erkennen, sodass Cyberkriminelle erst gar keine Chance haben. Mit praxisnahen Tipps für den privaten Alltag und das Berufsleben. Sie erfahren Wie Sie mit Ransomware und Malware umgehenWas Sie tun müssen, wenn Ihr Computer oder Smart-phone gestohlen wurdeWie Sie sicher in öffentlichen WLANs surfenWie sich KI auf Ihre Cybersicherheit auswirkt Joseph Steinberg ist Berater für Cybersicherheit und neue Technologien. Er schreibt den offiziellen Leitfaden, aus dem viele Chief Information Security Officers (CISOs) für ihre Zertifizierungsprüfungen lernen, und ist als einer der Top-3-Cybersicherheits-Influencer weltweit anerkannt
The Vibe Coding Playbook
A DETAILED AND UP-TO-DATE WALK-THROUGH FOR ENTREPRENEURS WITH LIMITED (OR NON-EXISTENT) CODING SKILLS WHO WANT TO BUILD PROFITABLE SOFTWARE COMPANIES USING GEN-AI TOOLS The Vibe Coding Playbook: Building Your Tech Business with AI, by AI and data science educator Siraj Raval, translates complicated technical concepts into accessible, easy-to-implement strategies for professionals and entrepreneurs interested in building profitable tech businesses without spending years learning to code. Raval reveals how AI code assistants like Cursor function as virtual “co-founders,” enabling non-technical entrepreneurs to create valuable software products and services that generate reliable recurring revenue in the real world. The Vibe Coding Playbook walks you through every stage of building an AI-powered business: from conceptualizing ideas to building minimally viable products (MVPs), iterating after launch, and scaling lean operations that help maintain healthy profit margins. The author provides practical frameworks, real-world case studies of successful non-technical founders, and step-by-step guidance for navigating the technical aspects of deployment, testing, and growth—all without requiring traditional programming expertise. Perfect for ambitious entrepreneurs who lack extensive technical and programming skillsets, The Vibe Coding Playbook is a detailed roadmap to capitalizing on the current AI gold rush. A detailed and up-to-date walkthrough for entrepreneurs with limited (or non-existent) coding skills who want to build profitable software companies using new gen-AI tools In The Vibe Coding Playbook: Building Your Tech Business With AI, renowned AI and data science educator Siraj Raval walks you through exactly what you need to do to build a technology business with generative AI-powered code assistants. Raval offers step-by-step guidance for non-technical professionals and entrepreneurs interested in creating scalable, profitable enterprises without spending years learning how to code. This book conceives of new artificial intelligence tools, like Cursor, as “co-founders,” lighting your way to constructing valuable software products and services. You’ll learn to build minimally viable products (MVPs), iterate on your software products as you develop and after launch, and grow your company while maintaining a lean, efficient, solopreneur-focused structure. Inside the book: Detailed guidance for entrepreneurs interested in creating powerful tech solutions for niche problems and markets without hiring expensive software developersStrategies for using generative AI tools to substitute for traditional technical co-foundersIllustrative case studies from real-world founders who built successful technology businesses without learning to codeUseful tools for non-technical entrepreneurs, including prompt libraries, decision trees, QR codes linking to video tutorials demonstrating key techniques, and access to an exclusive online community of like-minded founders Perfect for ambitious professionals and entrepreneurs who want to build a successful technology company now – using commercially available AI tools – The Vibe Coding Playbook is your personal roadmap to creating useful and profitable software for customers without learning how to code. SIRAJ RAVAL is an AI and data science educator. With millions of combined followers on YouTube and GitHub, he teaches students around the world how to implement the latest artificial intelligence and data techniques, tools, and platforms. He specializes in making complex technical concepts easily accessible to beginners.
Vergleich der Implementierungsmethoden für relationale Datenbanken in AWS VPC
German: Diese Masterarbeit vergleicht drei Methoden zur Implementierung relationaler Datenbanken in einer Amazon Web Services Virtual Private Cloud (AWS VPC): die manuelle Konfiguration, die Infrastructure-as-Code-Bereitstellung mit Terraform und die KI-gestützte Implementierung mit Amazon Q. Ziel ist es, jene Methode zu identifizieren, die den größten Nutzen hinsichtlich Effizienz, Automatisierung und Fehlerminimierung bietet. Basierend auf dem Design Science Research (DSR)-Ansatz wurden drei Prototypen entwickelt und mittels Nutzwertanalyse (NWA) nach Kriterien wie Implementierungszeit, Wartbarkeit, Skalierbarkeit, Sicherheit und Automatisierungsgrad bewertet. Die Ergebnisse zeigen: Die manuelle Methode ist transparent, aber zeitaufwendig; Terraform bietet Konsistenz und Wiederholbarkeit; die Amazon Q-Methode erzielt durch KI-gestützte Generierung die höchste Effizienz und geringste Fehlerquote. Die Hypothese wurde bestätigt, dass die KI-basierte Implementierung mit Amazon Q erzielt den höchsten Gesamtnutzen und stellt das zukunftsorientierteste Modell dar. Die Arbeit verdeutlicht, dass die Verbindung von Automatisierung und Künstlicher Intelligenz neue Maßstäbe für Effizienz und Qualität in Cloud-Deployments setzt. English: This thesis compares three methods for deploying relational databases within an Amazon Web Services Virtual Private Cloud (AWS VPC): manual setup, Infrastructure-as-Code with Terraform, and AI-assisted deployment using Amazon Q. The aim is to determine which approach delivers the greatest efficiency, automation, and reliability. Following the Design Science Research (DSR) framework, three prototypes were developed and assessed through a Utility Analysis using criteria such as implementation time, maintainability, scalability, security, and automation level. Results show that the manual method is transparent but slow, Terraform offers reproducibility, and Amazon Q achieves the highest efficiency through AI-driven code generation. The hypothesis was confirmed that the AI-assisted Amazon Q approach provides the highest overall benefit and represents the most future-oriented model. The thesis demonstrates how combining Automation and Artificial Intelligence defines a new standard for intelligent and efficient cloud deployment.
Die unternehmensinterne IT-Revision und die weiterbildungsimpliziten Herausforderungen der nächsten Jahre
Ziel der Arbeit ist es, die besondere Schwierigkeit bei der Qualifizierung geeigneten Personals in der IT-Revision hinsichtlich adäquater Datenanalysen herauszuarbeiten. Mit zunehmender IT-Durchdringung, die heute Standard ist, jedoch durch den zukünftigen Einsatz von Künstlicher Intelligenz und Data und Process Mining in den operativen Bereichen wie im Energiehandel / Trading und anderen forciert wird, ist es notwendig, auch in der Internen Revision geeignetes Personal zu finden und zu halten. Es muss sichergestellt werden, dass auch zukünftig im Bereich des managementrelevanten Prüf- und Beratungswesens geeignete Beschäftigte die Skills mitbringen oder aufbauen, die für die Beurteilung eines ordnungsgemäßen IT-Einsatzes im Unternehmen benötigt werden. Die vorliegende Arbeit referiert über heutige besondere Anforderungen an IT-Revisoren und arbeitet heraus, welche Möglichkeiten, aber auch Herausforderungen bestehen, dieses Personal adäquat zu fördern und zu motivieren. Insbesondere das Thema Datenanalyse von Datenbeständen aus operativen Großsystemen – am Beispiel von SAP und speziell IS-U – sowie eine beispielhafte empirische Erhebung zum Qualitätsniveau von IT-Revisoren mehrerer Energieversorgungsunternehmen rundet das bestehende Bild ab.
Machine Learning in Nanoelectronics
Bridge the gap between advanced algorithms and hardware innovation with this essential book, which details how machine learning is being used to overcome challenges in nanoelectronics while laying the critical groundwork for the future of neuromorphic computing hardware. New techniques for obtaining insights from enormous amounts of data and efficiently acquiring smaller data sets are provided by recent developments in machine learning. Researchers in nanoscience and nanoelectronics are experimenting with these tools to tackle challenges across many fields. Nanoscience and nanoelectronics not only advance machine learning but also lay the groundwork for neuromorphic computing hardware to broaden machine learning algorithm implementation. This book is a collection of possibilities for machine learning in nanoelectronics, semiconductor devices, and based circuits. With an easy-to-understand approach, this book explores the latest in machine learning in nanoelectronics materials and nanoscale devices through insights and analysis of recent developments in nanoelectronics. Ashish Maurya, PhD is an Assistant Professor in the Electronics and Communication Engineering Department and Assistant Dean of Research and Development at the Kanpur Institute of Technology. He has published nine journal articles and seven international conference proceedings. His current research interests include machine learning in semiconductor physics, nanoelectronics, and emerging semiconductor materials and their applications in various analog and digital circuits. Mandeep Singh is a Professor in the Electronics and Communication Engineering Department at the Indian Institute of Information Technology. He has published three books, five book chapters, and various research papers in international journals. His areas of research include semiconductor device modeling, memory design, and low-power VLSI design. Balwinder Raj, PhD is an Associate Professor at the National Institute of Technology Jalandhar. He has authored and co-authored ten books, 15 book chapters, and more than 150 research papers in peer-reviewed national and international journals and conferences. His areas of interest include classical and non-classical nanoscale semiconductor device modeling, nanoelectronics, FinFET-based memory design, and low-power VLSI design.
Data Science First
A detailed, up-to-date walkthrough for implementing language models in data science applications In Data Science First: Using Language Models in AI-Enabled Applications, the Chief AI Officer at Intersect AI, John Hawkins, sets out the critical challenge facing data scientists today: how to effectively integrate powerful language models into their workflows while adhering to data science principles that ensures your data generates reliable conclusions. Hawkins provides a practical roadmap for leveraging these revolutionary tools while maintaining the analytical rigor that separates successful implementations from costly failures. This guide skips hype and jargon, focusing instead on nine proven strategies for applying language models in real-world data science projects. From exploiting semantic vectors and few-shot prompting to synthetic data generation and developing agentic AI applications, Data Science First presents concrete design patterns that remain relevant despite rapidly evolving technologies. Each approach is illustrated with detailed case studies, including complaint processing and resume filtering, demonstrating how to evaluate model performance, handle failure modes, and deliver measurable business value. Data Science First is perfect for data scientists interested in enhancing their traditional statistical and machine learning skills with modern AI capabilities. It’s also a must-read for software engineers building language model-powered products and technical managers interested in deploying these tools reliably. Proven, practical techniques for integrating language models into your data science workflows Data Science First: Using Language Models in AI-Enabled Applications, by Intersect AI’s Chief AI Officer John Hawkins, explains how practicing data scientists can integrate language models in data science workflows without abandoning essential principles of reliability, accuracy, and efficacy. Hawkins offers crystal-clear guidance on when, where, and how data scientists can integrate language models into their existing workflows without exposing themselves or their companies to unnecessary risks. This guide walks you through strategic design patterns for incorporating language models into real-world data science projects. It avoids strategies and techniques that rely heavily on proprietary tools that are likely to evolve very quickly (or could disappear entirely) in the near future. Instead, the author presents foundational methodologies that will remain valuable regardless of how individual platforms or services change. The book combines sound theory with practical case studies that cover common data science projects in the education, insurance, telecommunications, media and banking industries. Including customer churn analysis, customer complaint routing and document processing, demonstrating how language models can enhance rather than replace traditional data science methods. You’ll find: Three chapters providing a solid grounding in the ideas, principles and technologies that are used for data science with language modelsNine chapters that discuss specific patterns for integrating language models into data science workflows, including semantic vector analysis, few-shot prompting, retrieval-based applications, synthetic data generation and AI agent developmentReal-world case studies discussing applications like fraud detection, customer churn, translation, document classification and sentiment analysis, with concrete business applicationsComprehensive evaluation methods and testing frameworks are discussed in the context of language model applications in enterprise environmentsPractical code examples and implementation guidance using popular tools like HuggingFace, OpenAI, Google Gemini, as well as more development frameworks like LangChain, and PydanticAIStrategic insights for balancing model accuracy, interpretability, and business requirements while avoiding common pitfalls in AI deployment An authoritative resource for data scientists and software engineers interested in using modern AI tools to build data-driven applications, Data Science First is a strategy guide for professionals navigating the discipline of data science as it is disrupted by generative AI. Whether you're looking to improve existing workflows or develop entirely new AI-powered solutions, you’ll discover how to use language models in ways that consistently add value. JOHN HAWKINS is the Chief AI Officer at Intersect AI, an organization that builds bespoke AI solutions to solve real workplace problems for companies in industries like insurance, media and healthcare. He leads the company’s data science initiatives, working with clients directly to analyze their workflow processes and design people centred AI systems.
Data Science mit Big Data
Bietet einen umfassenden Überblick zum Umgang mit großen Datenmengen. Erläutert verständlich die zentralen Big-Data- Konzepte und zeigt deren Möglichkeiten und Grenzen auf. Illustriert die Datenspeicherung mit skalierbaren NoSQL-Systemen und zeigt die verteilte Verarbeitung mit Batch-, Micro-Batch- und modernen Streaming-Verfahren. Beschreibt anwendungsorientiert, wie moderne Datenplattformen entwickelt und durch Künstliche Intelligenz erweitert werden können. Ihr exklusiver Vorteil: E-Book inklusive beim Kauf des gedruckten Buches Mit diesem Buch erhalten Sie einen praxisnahen und technologieunabhängigen Einstieg in den Umgang mit großen Datenmengen. Zahlreiche Beispiele werden mit gängigen Open-Source-Werkzeugen und modernen Container-Technologien (Docker) umgesetzt, sodass Sie Ihr neu erworbenes Wissen gleich ausprobieren können und der Umgang mit kommerziellen Produkten ebenfalls problemlos möglich wird. Das Buch startet mit den Herausforderungen, die sich durch die verteilte Verarbeitung von Daten ergeben, sobald diese nicht mehr auf einen Rechnerknoten passen. Sie lernen, wie Sie Big-Data-Analytics mit Verarbeitungsparadigmen wie Batch-, Micro-Batch- und Stream-Verarbeitung praktisch umsetzen können; ebenso wird auf die Vor- und Nachteile von NoSQL-Datenbanken eingegangen. Einblicke zur Visualisierung von Analyseergebnissen, in zufallsbasierte Big-Data-Algorithmen sowie in Referenz-Architekturen für den Aufbau skalierbarer Big-Data-Systeme runden das Buch ab. Dieses Lehrbuch und Nachschlagewerk ist ein verlässlicher Begleiter für Studium, Ausbildung und berufliche Praxis. AUS DEM INHALT // Einstieg ins Thema Verteilte Systeme Big-Data-Management Data Warehouse, Data Lake(house), Data Mesh NoSQL Verarbeitungsparadigmen Systemarchitekturen Algorithmen und Datenanalyse Visualisierung Systementwicklung, -test und –betrieb KI-Anwendungen Ausblick
Basics of LaTeX for Students
Basics of LATEX for Students: Why LATEX Beats Microsoft Word for Academic Work is a comprehensive, student-focused guide designed to help learners master professional academic writing using LATEX. Written for an interdisciplinary audience ranging from undergraduate students to doctoral researchers, this book explains not only how to use LATEX, but why it is the preferred tool for serious academic and research work. The book adopts a practical, application-driven approach, guiding readers from fundamental concepts to advanced workflows used in research papers, theses, dissertations, presentations, and professional documents. Real-world examples, step-by-step explanations, and discipline-neutral templates ensure that readers can immediately apply what they learn across STEM, Social Sciences, Humanities, and Management Studies. By emphasizing clarity, consistency, and efficiency, this book empowers students and researchers to focus on content and ideas rather than formatting struggles, making it an essential companion for modern academic writing.
E-Autos (Rheinwerk Verlag)
Der Ratgeber zum E-Auto-KaufDie Vorteile von E-Autos liegen auf der Hand: komfortables Fahren, günstiges Laden an der eigenen Wallbox, saubere Mobilität ohne das Verbrennen fossiler Rohstoffe. Aber was ist mit dem Reichweitenproblem, den unübersichtlichen Ladeabos und dem hohen Kaufpreis? In diesem Ratgeber bespricht Jörg Rippel alles, was Sie vor dem Kauf und auf den ersten Fahrten wissen müssen. Vom ersten Laden an der Autobahn bis zum Batteriemanagement, von der Ökobilanz bis zur effizienten Fahrweise im Alltag dreht sich alles um Ihre ganz persönliche Mobilitätswende. Technik verstehen Die Technik eines E-Autos ist sicher und ausgereift, funktioniert aber ganz anders als beim Verbrenner. Was Sie über die Batterie, Antriebstechnik und das Thermomanagement wissen müssen, finden Sie in diesem Technikratgeber. Effizient und sparsam unterwegs Ihr neues Auto ist schon effizient, daher lohnt sich eine clevere Fahrweise umso mehr. Schon kleine Änderungen in der Fahrweise senken den Verbrauch spürbar und sparen Energie und damit bares Geld. Zahlreiche Tipps und Hinweise zeigen Ihnen, wie Sie Ihr E-Auto im Alltag am besten nutzen und batterieschonend unterwegs sind. Das richtige Auto für Sie Wie findet man das richtige Modell? Lohnt sich eine PV-Anlage für mehr Autarkie oder brauchen Sie die große Reichweite eines Hybrids? Eine Marktübersicht nach Fahrzeugklassen, Checklisten für den Kauf eines neuen oder gebrauchten E-Autos, ein Vergleich der Ladekarten und -apps helfen Ihnen, eine fundierte Kaufentscheidung zu treffen. Aus dem Inhalt: Mythen und Fakten zu E-Autos Laden leicht gemacht: die ersten Schritte Von der Verbrennung zum Elektromotor Batterie und Antrieb Ladetechnik- und Infrastruktur Marktüberblick und Kaufberatung Sparsam fahren Ladekarten, Netze und Roaming Ökobilanz und erneuerbare Energie Checkliste für den Kauf eines E-Autos Zum Autor:Jörg Rippel ist aus- und fortgebildet in Elektronik, Funktechnik und Informatik. Nach rund zwanzig Jahren Berufserfahrung, zuletzt in der Luft- und Raumfahrt, widmet er sich dem Schreiben von Sachbüchern zu technischen Themen.
Computer Memories 2
Memory, the second function of a computer, has gradually become more complex in order to meet growing needs in terms of capacity, speed, security and energy efficiency. It takes the form of a component or system, such as a memory subassembly or a mass storage device. For several decades, there has been a strong trend towards integrating memory directly into the processor under the term embedded memory, of which cache memory is a typical example. Understanding its internal mechanisms and interfacing is essential for mastering the operation of a computer and programming it efficiently. Computer Memories 2 focuses on the static storage cell and its associated component, known as static random-access memory. The first part presents the main temporal characteristics of a generic random-access memory, as well as its electrical and mechanical properties, including aspects related to encapsulation. The second part focuses on the study of the first category of random-access semiconductor memory: asynchronous static memory, commonly referred to by the acronym SRAM (static random-access memory). The book details its temporal, electrical and mechanical characteristics, providing an in-depth understanding of its operation and technical specifications. Philippe Darche is Lecturer in Computer Science at the University Institute of Technology (IUT) of Paris – Rives de Seine and Researcher in the Distributed Algorithms and Systems (DeLyS) team at LIP6, Sorbonne University, France. He is also the author of fifteen books on computer architecture.
Python für KI- und Daten-Projekte
ERSTE SCHRITTE MIT PYTHONJohannes Schildgen holt Sie genau da ab, wo Sie stehen: egal, ob Sie ganz neu in der Welt der Programmierung sind oder schon Grundkenntnisse vorweisen können. Steigen Sie direkt mit Praxisbeispielen ein, die echte Probleme aus Ihrem Arbeitsalltag lösen.DATENANALYSE UND -VISUALISIERUNGMithilfe von Jupyter Notebooks und Visual Studio Code schreiben Sie Skripte, mit denen Sie vorhandene Daten aufbereiten und lästige Routineaufgaben automatisieren können. Mithilfe von Web-Scraping lernen Sie auch, eigene Datenkorpora zu erstellen, mit denen Sie weiter arbeiten können MACHINE LEARNING UND KIBinden Sie KI effektiv in Ihre Arbeit ein und lernen Sie, wie Sie ChatGPT und Co. wirklich nutzbringend verwenden können. Mathematische Spezialkenntnisse über die Funktionsweise von KI benötigen Sie übrigens nicht – die Beispiele bleiben problemlösungsorientiert und praxisnah.DATENANALYSE FÜR EINSTEIGER – MIT PYTHON UND KIPython programmieren lernen, ohne sich mit »Hello World« aufzuhalten! In diesem Buch finden Sie praxisnahes Python- und KI-Wissen, das Sie auf Ihre Daten und alltäglichen Probleme anwenden können. Python ist die perfekte Lösung für die Datenanalyse. Der Autor lässt Sie keine lästigen Fingerübungen absolvieren, sondern zeigt Praxisfälle, die Sie für Ihre Zwecke adaptieren können. Anhand konkreter Beispiele lernen Sie die Programmierung mit Python und bekommen hilfreiche Tools für Uni, Wissenschaft und Büro an die Hand. KI einsetzen, Daten analysieren, Routineaufgaben automatisierenMit wichtigen Werkzeugen wie Jupyter Notebooks, pandas und MatplotlibFür Daten aus Excel, Datenbanken, Web-Scraping, XML und vielen mehr Aus dem Inhalt: Loslegen mit Python – ein erstes ScriptArbeiten mit DateienDatenanalyse und -visualisierungMachine Learning und KIDatenbankenAPIs nutzenWeb-Projekte erstellen
Predictive Methods in Next-Generation Computing
Predictive Methods in Next-Generation Computing is essential for anyone looking to understand how next-generation computing technologies are driving predictive models to create smarter, safer, and more sustainable solutions across diverse fields. Computing technologies are significantly evolving in all sectors, aiming to provide an automated, energy-efficient solution for various real-world problems with an advanced predictive model. This book explores computing technologies in various domains and provides novel strategies and designs for a smart, secure, and sustained environment for the future. Predictive Methods in Next-Generation Computing provides a realistic overview of various computer technologies that have made drastic advances in the field of smart applications, resulting in smart agriculture, healthcare, traffic management, and sustainability. The book comprehensively covers predictive models using artificial intelligence, machine learning, and Internet of Things and considers these applications for various smart applications to create a safe, smart environment for everyone. The chapters include real-world case studies, giving readers a glimpse into the benefits of integrating technology into predictive analytics to make the practice more automated, energy-efficient, safe, and profitable. R. Sathiyaraj, PhD is an assistant professor in the Department of Computer Science and Engineering at GITAM University. He has authored two books, served as lead editor for two books, and published five patents and over 20 articles in refereed journals and international conferences. His research interests include machine learning, big data analytics, and intelligent systems. Rajesh Kumar Dhanaraj, PhD is a professor atSymbiosis International University. He has contributed over 45 books on various technologies, 21 patents, and over 90 articles in various refereed journals and international conferences. His research interests include machine learning, cyber-physical systems, and wireless Sensor networks. K. Arun Kumar, PhD is an assistant professor in the Department of Computer Science and Engineering at GITAM University. He has contributed over 15 articles to various reputed journals. His research focuses on wireless networks, data mining, deep learning, and big data. Rutvij H. Jhaveri, PhD is an Associate Professor in the Department of Computer Science and Engineering at Pandit Deendayal Energy University. He has published more than 200 papers in a wide variety of areas in computer scienceand coedited four books. His research interests include software-defined networking, cybersecurity, and smart ecosystems. A. Mohamed Abbas, PhD works in the IT Department at the University of Technology and Applied Sciences, Sultanate of Oman. He has published 23 research articles in reputed international journals and presented 16 papers in international Conferences. Additionally, he has registered four patents, one of which was granted. His research is focused on artificial intelligence, big data, Internet of Things, and machine learning.
Growth Engineering
BUILD SOFTWARE THAT REAL-WORLD CUSTOMERS LOVE TO USE WITH PROVEN, GROWTH-FIRST ENGINEERING STRATEGIES In Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World, software engineer, Rita Okonkwo, offers a modern, practical guide for anyone building products that need to thrive in real-world marketplaces. Okonkwo explains how to use clean architectures, thoughtful instrumentation, and Drawing from hands-on experience designing growth systems in large-scale environments, Okonkwo explains how to use clean architectures, thoughtful instrumentation, and experimentation frameworks to drive meaningful, sustainable product growth. She demonstrates how to build software that gains traction, fuels engagement, and supports continuous iteration using proven strategies—feature flighting, data-driven experimentation, logging, metrics, controlled rollouts, scalable data pipelines, real-time analytics, and more. Through practical examples and case studies, she shows how to break down complex systems, ship improvements quickly, and make confident product decisions grounded in data. Packed with actionable techniques for engineers and product leaders who want to create a growth-first engineering culture, Growth Engineering is a must-read for software engineers, product managers, developers, entrepreneurs, and founders—anyone committed to creating commercially successful software that customers love. Build software that users actually use with proven growth-oriented software development strategies In Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World, experienced software engineer with the Microsoft Experiences + Devices Growth team, Rita Okonkwo, delivers a strategic guide for anyone interested in building tech products that scale organically through smart technical choices. You'll learn how clean architecture, thoughtful instrumentation, and experimentation frameworks directly influence growth outcomes. With a focus on practical systems and real-world decision-making, this book shows how to build software that gains traction, drives engagement, and supports continuous iteration. You'll learn all about key growth engineering strategies like feature flighting, data-driven experimentation, logging, and metrics tracking. You'll find real-world case studies that break down design systems that support rapid iteration, and data-based product decision-making. Inside the book: Why growth engineering matters and how engineers can get directly involved in itExperimentation strategies, including controlled rollouts and effective A/B testing techniquesHow to build scalable data pipelines and integrate real-time analyticsWays to create a growth-first engineering culture, generating faster iterations without sacrificing quality Perfect for software engineers, product managers, and developers interested in building products that users love, Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World is a must-read for entrepreneurs, founders, and other technology business leaders ready to discover how to consistently create commercially successful software. RITA OKONKWO is a software engineer specializing in growth systems. She works in Microsoft’s Experiences + Devices organization, where she focuses on experimentation, intelligence, and scalable growth infrastructure. She combines academic insight with real-world engineering experience to bridge the gap between technical systems and product strategy.
Designing Inclusive Classrooms
Equip yourself with the essential conceptual frameworks and actionable strategies needed to address digital disparities and leverage cutting-edge technologies like AI and virtual reality for creating truly equitable and socially just learning environments. Recently, both the education and technology fields have acknowledged a rising awareness that traditional teaching practices, curricula, and institutional policies can frequently uphold inequalities. These disparities have a greater impact on students belonging to marginalized communities, such as low-income backgrounds, students with disabilities, and racial and ethnic minorities. The global transition to digital and online learning has worsened this problem, highlighting disparities in access and inclusivity. This book will serve as a comprehensive reference for understanding how to create equitable and socially just learning environments by leveraging emerging technologies. The book will cover a wide range of topics, from foundational theories of equity and justice in education to practical applications of cutting-edge technologies such as artificial intelligence, virtual reality, and digital learning platforms. It will provide both a conceptual framework and actionable strategies for designing classrooms that meet the diverse needs of students while promoting fairness, inclusivity, and engagement. In addition to practical guidance, the book will critically examine the challenges and ethical considerations of using technology in the pursuit of equity and social justice. This book will serve as an invaluable resource across the fields of education and technology to create a more equitable future. Designing Inclusive Classrooms Recently, both the education and technology fields have acknowledged a rising awareness that traditional teaching practices, curricula, and institutional policies can frequently uphold inequalities. These disparities have a greater impact on students belonging to marginalized communities, such as low-income backgrounds, students with disabilities, and racial and ethnic minorities. The global transition to digital and online learning has worsened this problem, highlighting disparities in access and inclusivity. This book will serve as a comprehensive reference for understanding how to create equitable and socially just learning environments by leveraging emerging technologies. The book will cover a wide range of topics, from foundational theories of equity and justice in education to practical applications of cutting-edge technologies such as artificial intelligence, virtual reality, and digital learning platforms. It will provide both a conceptual framework and actionable strategies for designing classrooms that meet the diverse needs of students while promoting fairness, inclusivity, and engagement. In addition to practical guidance, the book will critically examine the challenges and ethical considerations of using technology in the pursuit of equity and social justice. This book will serve as an invaluable resource across the fields of education and technology to create a more equitable future. Ashwin Fernandes, PhD is the Executive Director of Amesa Engineering. He founded India’s first nationwide private sector evaluation framework called QS I-GAUGE. He has met with senior leaders and is often invited to speak at conferences and events on university rankings, ratings, education, and higher education. Raul Villamarin Rodriguez, PhD is a Professor and the Vice President of Woxsen University. He has made significant research contributions with more than 800 publications to his credit. His research focuses on artificial intelligence, machine learning, and cognitive psychology. Thangam A., PhD is an Assistant Professor in the Department of English at the Dr. M.G.R. Educational and Research Institute. She has three journal publications to her credit. Sunethra P.K. Thennakoon, PhD is a Professor of Geography and the Director of the International Center for Multidisciplinary Studies at the University of Sri Jayewardenepura. She has more than 70 publications in international journals and conferences. Her research interests include strategic and organizational management and entrepreneurship. Xiaochen Zhang, PhD is Chief Responsible AI Officer and Executive Director at AI 2030 and Global Head of Innovation and Go-to-Market at Amazon Web Services with more than 20 years of experience. He is the founder and CEO of FinTech4Good and an investor at Gaingels, focusing on FinTech and AI ventures. He is a frequent speaker on responsible AI, blockchain, digital finance, policy, climate innovation, and impact investing. Hemachandran Kannan, PhD is the Associate Dean and Director of the AI Research Center at Woxsen University. He has more than 130 publications to his credit, including articles in international journals and conferences. His research interests include artificial intelligence, machine learning, VLSI design, and embedded systems.
Making AI Work for People
Build AI applications that inspire trust in your users and solve real-world problems Making AI Work for People: A Framework for Designing and Building Impactful AI-Powered Applications empowers software engineers, product managers, and app designers to create AI-enabled applications that inspire trust, solve hard problems, and prioritize human needs. Written by Asmaa Ibrahim, Senior AI Consultant at Google and leader of the Gemini in Firebase product launch, this book shows you how to use rapid technological advancement to build successful products that your users can’t get enough of. At the heart of the book is the PRESS framework, a systematic approach ensuring AI products embody five essential principles: People-first design, Responsible in the core, Explainability and interpretability, Safety via benchmarking, and Sustainability and long-term impact. The book demonstrates how to identify AI integration opportunities, design human-centered solutions, measure success with business-focused KPIs, and scale from MVP to production-grade systems while managing costs and infrastructure challenges. This comprehensive guide is essential for software engineers integrating AI into product development, product managers leading AI initiatives, and app designers creating human-AI interfaces. It’s a must-read for executives and technical team managers seeking systematic approaches to responsible AI development that drive measurable business outcomes while maintaining trust, accessibility, and ethical integrity throughout the product lifecycle. Design AI applications that inspire trust, solve real problems, and put people first Making AI Work for People: A Framework for Designing and Building Impactful AI-Powered Applications by Asmaa Ibrahim offers software engineers, product managers, and app designers a comprehensive framework for creating AI-powered applications that truly serve humanity. Ibrahim, Product Lead at Aleph Alpha, experienced ML team leader, AI-driven transformation expert, and leader of the team that created Gemini in Firebase, addresses the critical challenge facing today's tech professionals: how to harness AI's transformative potential while guaranteeing applications remain ethical, accessible, and aligned with human values. This book explores cutting-edge innovation and provides the PRESS framework for designing human-first solutions that enhance – rather than replace – human capabilities. The PRESS framework is a systematic approach that guarantees AI-powered products are designed with five essential principles in mind: People-first design, Responsible in the core, Explainability and interpretability, Safety via benchmarking, and Sustainability and long-term impact. The author offers real-world case studies, proven development methodologies, and insights from successful AI implementations across the telecom, retail and other sectors, showing you how to navigate the complex landscape of AI integration while remaining laser-focused on user needs, trust, and ethics. The book covers everything from identifying opportunities for AI integration and measuring success with human-centered KPIs to scaling applications from MVP to production-grade systems. Making AI Work for People also includes: A comprehensive PRESS framework that guides every stage of AI product development from conception to deploymentPractical strategies for human-AI collaboration that amplify rather than replace human strengths and capabilities in real-world applicationsA proven evaluation flywheel to guarantee that AI applications meet real user needsReal-world case studies from telecom and retail demonstrating successful human-first AI implementationsEssential guidance for scaling AI applications while managing costs, infrastructure challenges, and long-term maintenance requirements This book is essential for software engineers integrating AI into product development, product managers leading AI initiatives, and app designers creating human-AI interfaces who want to build applications that inspire trust and deliver genuine value through the PRESS framework. It's also a must-read for executives and managers of technical teams seeking crucial insights into systematic, responsible AI development practices that drive measurable business outcomes. ASMAA IBRAHIM is a Senior AI Consultant at Google where she led the Gemini in Firebase product launch. She has more than 15 years’ experience in tech, including 9 years dedicated to AI-driven digital transformation efforts. She’s an expert in software engineering, UX collaboration, and business leadership.
The AI Cybersecurity Handbook
The essential framework for integrating artificial intelligence into modern cybersecurity strategy In The AI Cybersecurity Handbook, Caroline Wong—recognized cybersecurity strategist, IANS Faculty Member, and Director at Teradata—defines the new strategic frontier where artificial intelligence and information security converge. Drawing on two decades of executive and technical leadership across governance, risk, compliance, and software security, Wong examines how AI is reshaping both attack and defense. She provides a practical yet forward-looking blueprint for implementing adaptive, AI-driven defenses; mitigating data and model risk; and embedding responsible AI principles into enterprise cybersecurity programs. Through detailed analysis and real-world examples, Wong reveals how threat actors are exploiting AI to accelerate and disguise attacks—and how leading organizations can counter with intelligent detection, predictive analytics, and autonomous response capabilities. Written for security executives, technical leaders, and business decision-makers, The AI Cybersecurity Handbook delivers a cohesive strategy for harnessing AI responsibly, strengthening resilience, and anticipating the next generation of cyber threats. An up-to-date exploration of how AI is transforming cybersecurity and how to implement AI in your organization’s cyber defenses to meet today’s evolving threat landscape In The AI Cybersecurity Handbook, author Caroline Wong examines AI’s impact on both offensive and defensive cybersecurity strategies. Wong explains how artificial intelligence has transformed the way cyberattacks are carried out and how technology professionals prepare for and defend against them. On the offensive side, the author explores how AI is being used to automate, accelerate, and personalize attacks against targets. She shows you how these technologies make cyberattacks cheaper to carry out, harder to detect, and easier to automate at scale--broadening the impact beyond initial targets to include partners, third parties, and critical connected systems. For defenders, this book demonstrates how to use AI to evolve past traditional, rules-based cybersecurity defense to an adaptive model that evolves in real-time to counteract emergent risks and novel attack vectors. These new systems rely on continuously updated data inputs and form resilient bulwarks against formidable threats. Inside the book: Hands-on strategies for minimizing operational barriers to AI implementation to enable new capabilities for defenders and allow for the use of powerful tools, like smart data enrichment, AI-powered query engines, and self-initiating security actionsUp-to-date discussions of how to use AI to accelerate your cybersecurity responses while improving their accuracy, including anomaly detection, threat identification, and dynamic mitigation processesExpert advice for building resilient AI-enabled cybersecurity without compromising on AI ethics Perfect for cybersecurity professionals, IT managers, developers and business leaders looking for actionable guidance on the integration of AI into their IT security practices, The AI Cybersecurity Handbook offers accessible strategies well-suited to people with varying levels of technical expertise. It’s a must-read book for everyone with a stake in safeguarding digital assets. CAROLINE WONG is a recognized cybersecurity strategist with nearly 20 years of experience in GRC, product security, and executive leadership. She serves as Director at Teradata and Faculty with IANS Research, advising Fortune 500 organizations on governance, risk, and resilience. Caroline is the author of Security Metrics: A Beginner’s Guide—inducted into the Cybersecurity Canon Hall of Fame (2022)—and hosts three industry podcasts: Humans of InfoSec, The AI Security Edge, and Wake Up. Her career spans leadership roles at eBay, Zynga, Symantec, Cigital, and Cobalt.
Ihr Smart Home mit Home Assistant (2. Auflg.)
Home Assistant strukturiert einrichten und individuelle Heimautomation sicher umsetzen Sowohl für Einsteiger als auch technisch versierte Anwender, die ihr Smart Home herstellerunabhängig und flexibel steuern möchtenSystematischer Einstieg von Installation und Konfiguration bis zur stabilen GeräteintegrationAutomationen mit Szenen und Blueprints entwickeln sowie eigene YAML-Skripte erstellenEigene Smart-Home-Geräte mit ESPHome einbinden und gezielt erweiternSicherheit, Backups und langfristig stabile Systemarchitektur Home Assistant einrichten und anpassen In diesem Quickstart-Guide erhalten Sie das nötige Wissen, um Home Assistant in Betrieb zu nehmen und Ihr Smart Home an Ihre persönlichen Bedürfnisse anzupassen. Hierbei liegt ein besonderer Schwerpunkt auf Sicherheit, Flexibilität und Stabilität. Ein kurzer Rundgang durch die Benutzeroberfläche macht Sie mit allen wichtigen Funktionen vertraut. Automation für Einsteiger und FortgeschritteneSie erhalten eine leicht verständliche Einführung in die Automation mit Szenen und Blueprints. Vielseitig einsetzbare Beispiele wie smarte App-Benachrichtigungen oder die Verarbeitung von Wetterdaten dienen als Ausgangspunkt für individuelle Automationen. Technisch versierte Nutzer erfahren, wie sie Skripte von Grund auf selbst erstellen und eigene Smart-Home-Geräte mit ESPHome einbinden. Aus der Praxis für die PraxisSie profitieren von der jahrelangen Erfahrung des Autors. Praktische Tipps und unverzichtbare Must-have-Einstellungen helfen Ihnen, typische Stolperfallen zu vermeiden und Ihre Heimautomation ganz nach Ihren Vorstellungen umzusetzen.Aus dem Inhalt: Installation und EinrichtungBenutzeroberfläche kennenlernenWichtige Einstellungen und individuelle KonfigurationSicherheit und BackupsNeue Geräte hinzufügenEntscheidungshilfe: ZigBee, Z-Wave, Matter, Thread oder WLANZonen, Bereiche, Etagen und LabelsAutomation mit Szenen und VorlagenSkripte und YAML-CodeEigene Smart-Home-Geräte mit ESPHomeCompanion App für Mobilgeräte Oliver Kluth beschäftigt sich seit 2018 täglich mit Home Assistant und teilt seine gemachten Erfahrungen und hierauf aufbauende Tipps unter smarterkram.de und auf YouTube. Als Familienvater liegt sein Fokus darauf, das eigene Smart Home in den bestmöglichen Einklang mit dem Familienleben zu bringen, ohne hierbei auf Komfort verzichten zu müssen.
Video Editing Made Easy with DaVinci Resolve 20
Micro-content drives today’s social media and business marketing, but poor editing and low-quality video can quickly turn viewers away. DaVinci Resolve helps you elevate your content using professional-grade technology trusted in Hollywood films, TV shows, and commercials. In this practical, beginner-friendly guide, Lance Phillips, a Blackmagic Design Certified Training Partner with over 30 years of teaching and industry experience, shows you how to create polished video content for social media, business, and the web using DaVinci Resolve. Selected sections of the book are presented in color to clearly illustrate visual workflows and key editing concepts. While the book is written using DaVinci Resolve 20, most workflows, techniques, and concepts apply to DaVinci Resolve 18 and later versions. You’ll learn how to create complete short-form videos using the Cut page, apply efficient editing techniques such as split edits, syncing externally recorded audio, and recording voiceovers directly into your project. You’ll also explore essential sound editing workflows and simple yet powerful visual effects, including green screen, split screen, and picture-in-picture. By the end of this book, you’ll be able to confidently edit, finish, and publish short-form videos to platforms such as YouTube, TikTok, and Vimeo. *Email sign-up and proof of purchase required
Digitale Notizen mit Goodnotes (2. Auflage)
Deine Notizen – digital, flexibel und überall dabeiMit Goodnotes erstellst und verwaltest du deine Notizen schnell und unkompliziert auf dem iPad oder iPhone. Andrea Rawanschad zeigt dir alle wichtigen Funktionen, die du für Schule, Studium, Beruf und Alltag brauchst. So kombinierst du Notizen, Fotos, gescannte Dokumente, PDF-Dateien und Sketchnotes mühelos zu deinem individuellen digitalen Notizbuch.Goodnotes Schritt für Schritt verstehenDie Autorin führt dich durch alle wichtigen Funktionen – vom Anlegen des ersten Notizbuchs über das Erstellen eigener Vorlagen und Sticker bis hin zur Datensicherung. Du lernst, Goodnotes optimal an deine Arbeitsweise anzupassen und kreativ zu gestalten. Ein farbiges Kapitel mit vielen Beispielen zeigt dir, wie du dein Notizbuch mit Farben, Schmuckschrift und Sketchnotes zum Unikat machst.Kreativ arbeiten und digital organisiert bleibenDieser praxisnahe Leitfaden unterstützt dich dabei, deine Notizen übersichtlich zu strukturieren, dich selbst zu organisieren und mit anderen zusammenzuarbeiten. Zahlreiche Tipps und Best Practices helfen dir, Goodnotes effektiv in deinen Alltag zu integrieren, auch plattformübergreifend zwischen Apple-, Windows- und Android-Geräten – für einen unbeschwerten Wechsel von der Papierwelt zur digitalen Welt.Aus dem Inhalt:Tipps für die passende HardwareNotizbücher anlegen und organisierenStiftwerkzeuge, Auswahlfunktion, Bilder einfügen und mehrEffizient arbeiten mit der Suchfunktion, Gliederungen, Favoriten und VorlagenImportfunktion und Dokumente scannenWhiteboards und Textdokumente nutzenLernkarten, Präsentationen und Online-ZusammenarbeitKreative Gestaltung mit Farben, Schmuckschriften und StickernInspiration für Mindmaps, Mitschriften, Kalender, Bullet Journals und mehrNotizen exportieren und druckenSchritt für Schritt auf digitale Notizen umsteigen
Artificial Intelligence and Biodiversity
Harness the power of the digital frontier to save our planet with this essential guide, which demonstrates how deep learning, genetic engineering, and AI-based robotics can be integrated to track biodiversity, restore genetic diversity, and rebuild fragile ecosystems with unprecedented precision. From satellite imagery to genetic sequencing, AI is helping researchers track biodiversity, predict ecosystem changes, and monitor endangered species with unprecedented precision. This book delves into the exciting ways that artificial intelligence (AI), particularly deep learning, is being used to analyze complex ecological data. It offers an in-depth look at how these AI-driven technologies are transforming how we approach biodiversity conservation on a global scale, examining the role of genetic engineering, guided by AI, in restoring genetic diversity and helping species adapt to rapidly changing environments. Additionally, the book highlights how AI is revolutionizing ecosystem restoration, using AI-based robotics and reinforcement learning to restore habitats such as forests, wetlands, and coral reefs. It looks at real-world applications where AI systems are actively being used to rebuild damaged ecosystems, suggesting new ways to restore balance to nature. Through a combination of practical case studies and theoretical insights, this guide serves as an essential resource for anyone interested in the future of conservation, whether you are an AI specialist, an environmental scientist, or simply someone passionate about protecting the planet. By blending the latest in AI research with real-world biodiversity challenges, this book paints a picture of a future where technology and nature work hand in hand to safeguard life on Earth. Umesh Kumar Lilhore, PhD is a Professor in the Department of Computer Science and Engineering at Galgotias University. He has authored ten books, more than 100 research articles in journals, and filed 50 patents across India and the UK. His research interests include artificial intelligence, machine learning, and software engineering. Sarita Simaiya, PhD is a Professor of Computer Science and Engineering at Galgotias University with more than 17 years of teaching and research experience. She has published more than 140 articles in international journals and conferences. Her research focuses on machine learning, Internet of Things, and AI. Surjeet Dalal, PhD is a Professor and researcher with more than 20 years of experience in teaching and research in Computer Science and Engineering. He has published more than 140 articles in international journals and conferences. His research interests include artificial intelligence and cloud computing. Martin Margala, PhD is the Director of the School of Computing at the University of Louisiana at Lafayette with more than 25 years of experience in teaching and research. He has more than 400 publications to his credit, including in international journal articles and conference proceedings. He specializes in exascale computing, ultra-high frequency design, and design for reliability.
Retrospektiven in der Praxis
Know-how, um Retrospektiven kreativer und erfolgreicher zu gestalten •Kompaktes Referenzwerk mit neuen Impulsen für die Vorbereitung, Durchführung und Nachbereitung von Retrospektiven •Leicht verständlich geschrieben mit vielen motivierenden Beispielen und Tipps aus der Praxis •Ein Werkzeugkoffer für Moderatoren, Facilitatoren und Change Agents Retrospektiven sind eine der tragenden Säulen eines erfolgreichen Change-Prozesses und eines der wichtigsten Werkzeuge, um die notwendigen kulturellen Veränderungen in Teams und in der Organisation zu initiieren und zu begleiten. Sie können überall dort eingesetzt werden, wo ein kontinuierlicher Verbesserungsprozess etabliert werden soll. Praxisnah und mit vielen Beispielen beschreibt der Autor, wie Retrospektiven vorbereitet, moderiert und ergebnisorientiert durchgeführt werden. Er geht dabei auch auf verteilte, lösungsorientierte und systemische Retrospektiven ein und zeigt typische Probleme und Fallstricke auf. Die 2. Auflage vertieft die Arbeit mit verteilten Teams, was beim Setting von Online-Gesprächen und -Meetings zu beachten ist sowie Change Management mit Retrospektiven und wurde um das Thema »Psychologische Sicherheit« ergänzt.