Search results for "Deep Generative Modeling"
Items we need to order for you
Wachstum durch den Einsatz von Generativer KI
Dieses Buch bietet einen fundierten Einblick in die Relevanz von Generativer Künstlicher Intelligenz (KI) im Marketing. Neben einer kompakten Darstellung der Funktionsweise und Grenzen von Generativer KI wird ein umfassender Überblick über KI-basierte Medienformate gegeben.Anhand von Praxisbeispielen und konkreten Auswahlkriterien erfahren Sie, wie Sie geeignete Anwendungsfälle für Generative KI identifizieren und in Ihre Marketingstrategie integrieren können. .
R 694.88
Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
R 1,955.66
R 458.00
Llms and Generative AI for Healthcare
Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley and Manish Mathur from Google's Healthcare and Life Sciences Industry team help you explore real-world applications of these technologies in healthcare, from personalized patient care and drug discovery to enhanced medical imaging and robot-assisted surgeries. You'll also learn the challenges of using these technologies--and the ethical implications of their application in this field. With this book, you will: Learn how LLMs and generative AI can help address and transform healthcare issues Explore the basics of LLMs and generative AI and learn how they work Learn how these technologies are being applied in healthcare today Understand several LLM and generative AI use cases Examine the ethics and challenges of applying LLMs and generative AI to healthcare Understand the potential use of LLMs and generative AI in healthcare in the near term and their prospects for the future
R 1,277.00
R 432.00
R 611.00
R 519.00
Data-Driven Evolutionary Modeling in Materials Technology
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc.Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms.This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
R 9,992.60
Generative AI for Remote Sensing of the Environment
This book explores the cutting-edge integration of generative artifical intelligence (AI) techniques to enhance environmental remote sensing, providing a comprehensive guide from foundational algorithms to practical applications. It explains how advanced AI technology can be used to improve the way we monitor and understand the environment from a distance, such as through satellites or drones. It starts with an explanation of basic algorithms behind generative AI and gradually moves to complex algorithms, showing how they can be applied to real-world environmental issues, such as tracking climate change, monitoring deforestation, and predicting natural disasters. FeaturesIncludes real-world examples and case studies showing how generative AI improves environmental monitoring.Provides step-by-step explanations of algorithms and their implementation.Explains complex concepts in simple and easy-to-understand language and introduces strategies to address environmental challenges using AI-driven solutions.Offers cutting-edge research and advancements in AI and remote sensing, including the application of generative AI models like GANs and VAEs.Applies to diverse fields such as urban planning, agriculture, and disaster management.Discusses ethical considerations and challenges when integrating AI with remote sensing.This book is for researchers and practitioners in environmental monitoring, urban planning, agriculture, and disaster management using remote sensing technologies and AI to address environmental challenges and sustainability. It is also intended for university professors, graduate, and postgraduate students in environmental science, geospatial analysis, computer science, and data science working on projects related to AI and remote sensing.
R 6,960.00
R 825.00
Energy Modeling and Computations in the Building Envelope
Energy Modeling and Computations in the Building Envelope instills a deeper understanding of the energy interactions between buildings and the environment, based on the analysis of transfer processes operating in the building envelope components at the microscopic level. The author:Proposes a generalized physics model that describes these interactions at the microscopic level via the macroscopic characteristics of the building envelopePresents mathematical models that utilize classical analytical tools and can be used to perform quantitative predictions of the consequences of the energy interactionsReveals easy-to-apply engineering methods concerning the design and inspection of the building envelope, taking into account the effects of energy on the envelopeEnergy Modeling and Computations in the Building Envelope provides comprehensive coverage of this environmentally and economically important topic, from the physics of energy transfer to its numerical estimation. The book is especially useful to those looking to increase building energy efficiency, decrease the consumption of primary energy carriers, and raise the ecological sustainability of construction products.
R 2,964.47
Regenerative Principles in Facilities Design and Urban Re-modelling in Developing Countries
In the face of rapid urbanization and environmental challenges, the developing world stands at a crossroads. Conventional sustainable design, often modelled on Global North precedents, is proving insufficient to address the deep-seated infrastructural and socio-economic complexities of these unique contexts. This book argues for a radical shift from simply minimizing harm to actively healing and restoring our communities and ecosystems through regenerative architecture.Moving beyond theory, this work provides a critical and practical roadmap for applying regenerative principles specifically in the challenging environments of developing nations. We delve into the core of what makes regeneration different, showcasing how it can produce net-positive energy and water systems, enhance biodiversity, foster social equity, and create economic value where it is needed most.The heart of this book lies in its actionable insights and real-world evidence. Through a series of detailed case studies-from innovative adaptive reuse in Ghana and decentralized renewable systems in Rwanda to community-driven projects in South Africa, we demonstrate viable solutions. These are contrasted with lessons from pioneering examples in Sweden, the UK, and Canada, not as blueprints to copy, but as sources of adaptable strategies.A significant focus is given to a deep-dive case study of Nigeria, offering a nuanced examination of its colonial architectural legacy, current urban pressures, and the emergent regenerative strategies that blend vernacular wisdom with modern innovation.This book is more than a reference; it is a call to action. It provides policymakers with frameworks for supportive regulations, offers practitioners design strategies and performance metrics, and equips students and researchers with a comprehensive conceptual foundation. By championing an approach that is both context-sensitive and aspirational, this book serves as an essential guide for anyone committed to building resilient, vibrant, and equitable urban futures in the developing world and beyond.
R 9,962.15
R 1,515.65
Generalization With Deep Learning: For Improvement On Sensing Capability
Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
R 3,957.00
Fairness in Generative AI
Build responsible AI systems with practical fairness benchmarks. Turn fairness principles into measurable requirements using metrics, human review, AI governance, CI gates, and drift monitoring across text, image, and multimodal AI.Key FeaturesTurn generative AI fairness principles into testable responsible AI requirementsBuild benchmarks and metrics for LLMs, image models, and multimodal generative AIOperationalize AI governance with human review, CI gates, drift monitoring, and scorecardsApply AI ethics to identify harms, evaluate trade-offs, and guide model releasesBook DescriptionA child asks an image model for a picture of a soccer player and gets a boy, every time. Across hiring, lending, education, and healthcare, fairness in generative AI can affect opportunity, dignity, and trust. This practical guide shows you how to measure fairness and build responsible AI practices across LLMs, image models, and multimodal generative AI systems.Move from AI ethics and fairness principles to engineering artifacts you can test and defend. Define harms and sensitive attributes, decide whose experience is in scope, and translate fairness trade-offs into measurable requirements. Then build a modular benchmarking framework for your ML and MLOps pipelines using versioned scenario and prompt libraries, quantitative metrics, qualitative rubrics, and human review.Put AI governance into practice by publishing actionable scorecards and dashboards, adding fairness checks to CI and release gates, monitoring drift, and preparing incident-response playbooks. Keep evaluation meaningful as models, data, and expectations change.A running soccer image-generator case connects the concepts to implementation, showing how responsible AI engineering moves from principles to measurable production controls.What you will learnTurn fairness principles into testable requirements for generative AIMap harms to representational, allocative, and procedural checksDesign versioned scenario and prompt libraries that protect real dataImplement fairness metrics for text, image, and multimodal AI outputsApply human review with rater training and inter-rater agreementPublish scorecards and dashboards stakeholders can act onGate model releases on fairness and monitor drift in productionRun incident-response playbooks that keep benchmarks credibleWho this book is forThis book is for people who build, ship, evaluate, and govern generative AI. ML and data engineers, application developers shipping LLM features, and responsible AI, trust-and-safety, and security engineers will gain hands-on value. Product managers and engineering leaders will develop a shared language for AI governance, while policymakers, regulators, researchers, standards contributors, and civil-society advocates will find structured ways to scrutinize deployed systems. A working grasp of ML evaluation and Python is all you need.
R 1,628.00
Deterministic and Stochastic Modeling in Computational Electromagnetics
Help protect your network with this important reference work on cyber security Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely-used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges. Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic-stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models. Readers will also find: A range of specific examples demonstrating the efficiency of deterministic-stochastic modelingComputational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and moreIntroduction to fundamental principles in field theory to ground the discussion of computational modelingDeterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.
R 4,559.00
R 702.00
Generative AI for Personalized Learning
The rapid evolution of generative AI (GenAI) is reshaping education, making personalized learning more adaptive, accessible, and effective than ever before. This book explores the profound ways in which GenAI-driven tools—such as GPT-4, transformers, and GANs—are transforming traditional teaching and learning paradigms. From real-time curriculum adaptation to AI-powered tutors, GenAI is not just enhancing education—it is redefining it.Through in-depth analysis and real-world case studies from Finland, Singapore, Japan, and the United States, this book examines how AI-driven learning environments foster student engagement and improve outcomes. It highlights the shift from one-size-fits-all education to truly individualized learning paths, where AI dynamically adjusts content, pacing, and feedback to each learner’s needs. Teachers, rather than being replaced, are empowered with intelligent tools that enhance their instructional strategies.The rise of AI in education also brings critical challenges. This book does not shy away from discussing ethical concerns, data privacy issues, and the risks of bias in AI-driven education. It provides a balanced perspective, offering strategies to ensure that GenAI serves all students equitably, promoting fairness and inclusivity in the classroom. Whether you are an educator, researcher, policymaker, or technology leader, Generative AI for Personalized Learning offers valuable insights into the future of education. It presents a forward-thinking vision—ne where AI and human expertise work in harmony to create a more responsive, scalable, and learner-centric educational experience.The future of learning is not coming—t is already here. This book is your essential guide to understanding and navigating this transformation.
R 6,680.18
R 815.00
R 794.00
R 1,221.00
R 762.00
R 1,527.00
R 1,301.00
Architecting Generative AI Applications
Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world.Key FeaturesTurn generative AI prototypes into production-ready applicationsMaster LLM evaluation, observability, and reliability engineeringDeploy and scale AI systems using LLMOps and modern DevOps toolsBook DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems.Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end you’ll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learnDesign offline and online evaluation strategies (including statistical A/B testing) and collect the right dataConvert AI prototypes into production-ready applications that are stable, scalable, & secureReduce maintenance effort with best practices in testing, configuration, and code readabilityImplement DevOps, MLOps, and LLMOps—what's common and what differs across these approaches for AI systemsBuild platform teams to scale enterprise-wide generative AI adoptionDefine reliability targets using SRE principles and statistical A/B testingWho this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems.
R 1,628.00
Generative Adversarial Networks for Cybersecurity:
Generative Adversarial Networks (GANs) play a crucial dual role in cybersecurity, serving both as powerful defensive tools and sophisticated attack vectors that security professionals must understand and counter. GANs are invaluable for generating synthetic datasets to train cybersecurity models when real attack data is scarce or sensitive, creating realistic network traffic patterns for testing intrusion detection systems, and augmenting threat intelligence by simulating various attack scenarios without exposing actual vulnerabilities.Exploring the application of GAN models in intrusion detection, anomaly detection, and cybercrime, Generative Adversarial Networks for Cybersecurity: Protecting Data and Networks covers how GANs can be applied to pinpoint security holes, vulnerabilities, viruses, malware, phishing attacks, and other security risks. It explains how advanced GANs integrated with such digital technologies as the Internet of Things (IoT), cloud-native computing, edge analytics, serverless technology, and blockchain to protect and secure data and information from security breaches. The book also discusses how GANs can identify outliers, performance bottlenecks, and other issues in cloud infrastructure modules, applications, and data. Other topics featured in the book include:GAN-based security's ethical and privacy concernsGANs and explainable AIBuilding trustworthy 6G networks with Generative Adversarial LearningIntrusion detection systems enhanced by GANsGANs are a valuable tool for enhancing cybersecurity efforts by generating synthetic data and images that can show unusual patterns in data. This book helps researchers, academics, and professionals realize the potential of this powerful tool by presenting the latest innovations and applications of GANs in cybersecurity.
R 5,165.00
R 811.00
R 764.00
Generative AI with Amazon Bedrock
Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standardsKey FeaturesLearn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist ArchitectsMaster the core techniques to develop and deploy several AI applications at scaleGo beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure.Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock.By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learnExplore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performanceExplore several architecture patterns for different business use casesGain insights into ethical AI practices, model governance, and risk mitigation strategiesEnhance your skills in employing agents to develop intelligence and orchestrate tasksMonitor and understand metrics and Amazon Bedrock model responseExplore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standardsWho this book is forThis book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.
R 1,465.00
R 916.00
Art and Ethics of Generative AI
Generative AI technologies are reshaping the way we create, communicate, and compute, presenting both unprecedented opportunities and profound challenges. As these systems increasingly influence decisions that shape our lives, it becomes clear that performance metrics alone cannot define their success. A deeper understanding of the intersection between technical innovation and human values is essential to navigate this transformative era.The Art and Ethics of Generative AI: Fairness, Transparency, and Human Values provides a balanced exploration of generative AI, combining technical insights with ethical considerations. It explains generative model architectures and learning paradigms in an accessible way, while addressing critical issues such as algorithmic bias, explainability challenges, and emerging regulatory approaches. Practical frameworks for responsible AI development are offered, alongside multidisciplinary perspectives that bridge technical and humanistic viewpoints. Abstract concepts are brought to life through practical illustrations, demonstrating how ethical considerations play out in real-world AI applications.Designed for a wide audience, the book serves AI practitioners seeking actionable guidance for building responsible systems, researchers exploring nuanced ethical dimensions, educators and students looking for comprehensive learning materials, and policymakers needing governance insights. It is an essential resource for anyone engaged in the rapidly evolving AI ecosystem.
R 5,165.00
