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Generative AI and Stochastic Thermodynamics
Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.
R 3,481.00
Generative AI on Google Cloud with LangChain
Turn challenges into opportunities by mastering advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud toolsKey FeaturesSolve real-world business problems with hands-on examples of GenAI applications on Google CloudLearn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethicsBuild and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AIPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe rapid transformation and enterprise adoption of GenAI has created an urgent demand for developers to quickly build and deploy AI applications that deliver real value. Written by three distinguished Google AI engineers and LangChain contributors who have shaped Google Cloud’s integration with LangChain and implemented AI solutions for Fortune 500 companies, this book bridges the gap between concept and implementation, exploring LangChain and Google Cloud’s enterprise-ready tools for scalable AI solutions.You'll start by exploring the fundamentals of large language models (LLMs) and how LangChain simplifies the development of AI workflows by connecting LLMs with external data and services. This book guides you through using essential tools like the Gemini and PaLM 2 APIs, Vertex AI, and Vertex AI Search to create sophisticated, production-ready GenAI applications. You'll also overcome the context limitations of LLMs by mastering advanced techniques like Retrieval-Augmented Generation (RAG) and external memory layers.Through practical patterns and real-world examples, you’ll gain everything you need to harness Google Cloud’s AI ecosystem, reducing the time to market while ensuring enterprise scalability. You’ll have the expertise to build robust GenAI applications that can be tailored to solve real-world business challenges.What you will learnBuild enterprise-ready applications with LangChain and Google CloudNavigate and select the right Google Cloud generative AI toolsApply modern design patterns for generative AI applicationsPlan and execute proof-of-concepts for enterprise AI solutionsGain hands-on experience with LangChain's and Google Cloud's AI productsImplement advanced techniques for text generation and summarizationLeverage Vertex AI Search and other tools for scalable AI solutionsWho this book is forIf you’re an application developer or ML engineer eager to dive into GenAI, this book is for you. Whether you're new to LangChain or Google Cloud, you'll learn how to use these tools to build scalable AI solutions. This book is ideal for developers familiar with Python and machine learning basics looking to apply their skills in GenAI. Professionals who want to explore Google Cloud's powerful suite of enterprise-grade GenAI products and their implementation will also find this book useful.
R 1,465.00
Generative AI for Connected and Autonomous Vehicles
Harness generative AI for connected and autonomous vehicle systems As generative AI reshapes intelligent transportation, engineers and researchers need rigorous frameworks for deploying these technologies in safety-critical systems. This book offers a comprehensive treatment of the models, protocols, and adaptation techniques required to bring large language models, vision-language models, diffusion models, and agentic systems into modern CAV architectures. The book progresses from generative foundations through multimodal representation, language-based interaction, agentic coordination, communication protocols, model adaptation, and validation and safety. Mathematical, probabilistic, and reinforcement-learning prerequisites are consolidated into dedicated appendices, allowing the main chapters to focus on system design and CAV-specific applications. Written by an award-winning researcher with more than 100 patents across connected vehicles and AI, readers will also find: Practical treatment of prompt engineering, retrieval-augmented generation, function calling, and tool use for grounding GenAI in vehicular contextChapters on agent architectures, multi-agent systems, orchestration frameworks, and MCP and A2A protocols for collaborative CAV intelligenceAdaptation and deployment techniques including supervised fine-tuning, reinforcement learning fine-tuning, and knowledge distillation for resource-constrained edge platformsDedicated chapters on simulation platforms, evaluation methodologies, and ethical and safety considerations for deploying generative AI in autonomous vehicles Automotive engineers, AI researchers, and graduate students at the intersection of machine learning and intelligent transportation will find this book indispensable. Readers from OEMs, mobility startups, or academic labs gain the foundations and practices needed to design, adapt, and validate generative AI in connected mobility.
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Understanding Generative AI Business Applications
This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity.The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting,Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems.In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success.What You Will LearnWhat are the technical elements that constitute the makeup of Generative AI products?What are the practical applications of Generative AI?How can algorithms be designed to automate and improve written communication?What are the latest Generative AI architectures and algorithms?Who This Book Is ForData scientists, data analysts, decision makers, and business executives interested in gaining an understanding of Generative AI products
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Generative AI for Web Development
Explore the world of Generative AI and understand why it matters. This book is divided into two parts, introducing tools such as ChatGPT, DALL-E, and will show you how to use them to build AI-powered web apps.The first part of the book describes Generative AI and covers the essential models and APIs from OpenAI. Legal, ethical, and security considerations are discussed to help you decide whether it is an appropriate tool for your projects. You’ll then review ChatGPT and see how to use it effectively for generating code. This is followed by a review of best practices, and tips and techniques for getting around the limitations of ChatGPT and other OpenAI APIs.The second part of the book provides practical guide to building a series of web apps with Next.js that showcase how to use the OpenAI APIs. For example, you’ll learn how to build a Story/Poetry generator, a language learning app, and a blog site with a custom Chatbot widget. The code for the web apps is generated using ChatGPT.When done with this book, you’ll have a clear understanding of Generative AI and be well on your way to building web applications powered by OpenAI APIs and Next.js.What You Will LearnAssess the legal, ethical, and security concerns with using Generative AI in web applications Review the latest APIs provided by OpenAI for generating text and imageUse ChatGPT to generate code for web projects, as well as tips and tricks to working around the limitations.Who This Book Is ForExperienced web developers and software engineers who know their way around HTML, CSS, and JavaScript, but have limited or no experience using Generative AI to build web applications.
R 1,368.00
Generative AI for Business Analytics and Strategic Decision Making in Service Industry
Generative AI is transforming industries by enhancing how businesses analyze data, generate content, and automate complex processes. Its ability to produce diverse, high-quality outputs in areas like marketing, customer engagement, and software development is reshaping productivity and innovation. By leveraging advanced models such as GANs and Transformers, organizations can unlock new insights, streamline decision-making, and personalize services at scale. Generative AI also plays a crucial role in detecting errors, managing knowledge, and mitigating harmful content, contributing to safer and more efficient digital environments. This technology not only accelerates growth but also democratizes access to powerful tools, fostering creativity and problem-solving across sectors. Generative AI for Business Analytics and Strategic Decision Making in Service Industry provides a comprehensive exploration of how generative AI techniques can revolutionize business intelligence and data analytics practices. It offers a detailed examination of innovative algorithms, methodologies, and tools for leveraging generative AI to extract actionable insights, drive innovation, and enhance decision-making processes within the service industry. Covering topics such as big data, generative adversarial networks (GANs), and productivity management, this book is an excellent resource for students, academicians, industry professionals, researchers, managers, and more.
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Generative Artificial Intelligence (AI) Approaches for Industrial Applications
"Generative Artificial Intelligence (AI) Approaches for Industrial Applications" explores the transformative potential of Generative AI technologies across various industries. With contributions from international scholars and experts, this book provides a comprehensive overview of the latest trends, mathematical foundations, and practical applications of Generative AI models. Key sections examine the fundamental concepts of Generative AI, including Generative Adversarial Networks (GANs) and their ethical and security considerations. Special attention is given to the revolutionary impact of Generative AI in healthcare technologies, clinical decision-making, and predictive maintenance within the manufacturing sector. Additionally, the role of Generative AI in FinTech, particularly in redefining business models and enhancing digital security, is thoroughly examined. This book features cutting-edge research on text summarization, age progression using GANs, and integrating AI with regulatory practices. This book is a vital resource for academics, professionals, and practitioners bridging the gap between theoretical AI frameworks and their real-world industrial applications, offering insights into how Generative AI is shaping the future of industries worldwide.
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Generative AI on Kubernetes
Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.Learn to run GenAI models on Kubernetes for efficient scalabilityGet techniques to train and fine-tune LLMs within Kubernetes environmentsSee how to deploy production-ready AI systems with automation and resource optimizationDiscover how to monitor and scale GenAI applications to handle real-world demandUncover the best tools to operationalize your GenAI workloadsLearn how to run agent-based and AI-driven applications
R 1,484.00
Intelligent Scene Modeling and Human-Computer Interaction
This edited book is one of the first to describe how Autonomous Virtual Humans and Social Robots can interact with real people and be aware of the surrounding world using machine learning and AI. It includes:· Many algorithms related to the awareness of the surrounding world such as the recognition of objects, the interpretation of various sources of data provided by cameras, microphones, and wearable sensors· Deep Learning Methods to provide solutions to Visual Attention, Quality Perception, and Visual Material Recognition· How Face Recognition and Speech Synthesis will replace the traditional mouse and keyboard interfaces· Semantic modeling and rendering and shows how these domains play an important role in Virtual and Augmented Reality Applications.Intelligent Scene Modeling and Human-Computer Interaction explains how to understand the composition and build very complex scenes and emphasizes the semantic methods needed to have an intelligent interaction with them. It offers readers a unique opportunity to comprehend the rapid changes and continuous development in the fields of Intelligent Scene Modeling.
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Integrating Generative AI in Education to Achieve Sustainable Development Goals
A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI’s rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education. This book's primary objective is to serve as a resource for academicians, independent researchers, research scholars, students, scientists, libraries, and industry experts. Through a diverse range of topics, it investigates how generative AI can advance Sustainable Development Goals, making education more accessible, individualized, and effective. By harnessing the power of AI, the book argues, education can act as a catalyst for positive change, empowering individuals and fostering progress toward a sustainable and equitable future. The recommended topics within each part provide a structured framework for deepening the understanding of Generative AI's role in shaping the future of education, making this book a valuable reference for scholars and practitioners alike.
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Deep Learning in Genome Mapping
The book aims to develop methodologies, and throw light on empirical research advances of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.
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AI and Precision Medicine in Infectious Disease Management
This book presents technical advancements of artificial intelligence (AI)–based technologies and computational biology approaches, highlighting their applications in developing smart healthcare and precision medicine for managing infectious diseases. It discusses cutting-edge AI tools, particularly machine and deep learning models, and generative AI to assist in disease prediction and prevention, disease surveillance and diagnosis, drug discovery, drug design and development, healthcare, personalized medicine, pharmacology, and vaccine development. The contents fit the goal of establishing "Good Health and Well-Being," which is one of the "Sustainable Development Goals (SDGs) 2030."Key Features:• Includes AI/omics approaches in precision medicine and smart healthcare• Summarizes AI-driven drug and vaccine developments and AI tools for infectious disease prevention for clinicians, researchers, and policymakers• Proposes AI-based strategies for disease prediction, surveillance, and diagnosis
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Proceedings of International Conference on Generative AI, Cryptography and Predictive Analytics
The book presents the proceedings of the International Conference on Generative AI, Cryptography and Predictive Analytics (ICGCPA 2024), held at VIPS-TC, School of Engineering and Technology, Pitampura, Delhi, India, during June 28 – 29, 2024. It covers Generative AI's role in problem-solving, examining applications in image synthesis, content creation, healthcare, and optimization challenges. This book is a valuable resource for postgraduate students in various engineering disciplines.
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Generative AI in Higher Education: Ethical Governance, Skills, and Employability
Building upon the foundational principles established in the first volume, this second volume of Generative AI in Higher Education advances the debate on AI beyond theoretical frameworks toward concrete institutional practice. Where the first volume developed guiding principles for higher education institutions to use GenAI effectively and ethically in teaching and learning, this volume introduces original, research-informed frameworks that redefine employability as an ethical, adaptive, and human-centred practice. It presents distinctive models including Skills Dynamics, the Justice–Responsibility Framework for Human Flourishing, and the AI Dialogic Partner frameworks that move decisively from principles to practice.The book integrates ethical governance, curriculum design, and skills formation through actionable models such as the Double ERL Framework (Ethical Regulation Literacy and Ethical Reflexive Learning), the ERL Justice Framework, and the Institutional AI Stewardship Lab (IASL) model. Uniquely combining philosophical depth with policy relevance, it bridges global and local perspectives—drawing on UNESCO, OECD, and regional policy frameworks—to address the ethical, social, and regulatory dimensions of AI in higher education. The volume includes comprehensive international policy analysis, diverse global case studies, and practical tools that transform conceptual exploration into implementable guidance.Generative AI in Higher Education: Ethical Governance, Skills, and Employability serves as essential reading for university leaders, policymakers, educators, and employers seeking to align AI innovation with justice, inclusion, and sustainability. Its practical frameworks and transparency principles empower institutions to position education not merely as a response to technological change but as a transformative force for human flourishing, providing both scholarly contribution and actionable roadmap for embedding ethical AI in higher education practice.
R 5,165.00
Building Generative AI-Powered Apps
Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production.The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included.By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business.What You Will LearnWhat is Generative AI?What is ChatGPT and GPT4?What are language models and diffusions models?How do we deploy LangChain and HuggingFace?Who This Book Is ForSoftware engineers with a few years of experience building applications in any language or infrastructure
R 1,140.00
Generative AI for Film and VFX
This book provides a rigorous, practice-led introduction to generative AI for screen-based media, combining foundational theory with applied workflows grounded in professional production environments.Written by an experienced visual effects supervisor and educator, the book offers an authoritative framework for understanding and responsibly adopting AI technologies within contemporary visual effects pipelines. It is structured around six core domains: computer science fundamentals of Artificial Intelligence and machine learning, hardware and performance considerations, ethics and legal responsibilities, practical use of the open-source ComfyUI framework, professional integration with NUKE, and the broader ecosystem of community-driven tools and resources. Key concepts, including neural networks, diffusion models, latent space, ControlNets, and LoRAs, are explained from first principles and contextualized for creative practice. Particular attention is given to authorship, copyright, data provenance, and ethical use, ensuring readers can critically assess both the capabilities and limitations of generative systems.Intended for academic libraries, educators, researchers, and industry professionals, this book functions as both a teaching resource and a long-term reference. It supports curricula in visual effects, film production, digital media, and creative technologies, while also serving practitioners seeking a structured, open-source-oriented approach to integrating generative AI into professional workflows with technical clarity and ethical awareness.
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Unlocking Data with Generative AI and RAG
Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
R 1,465.00
Generative AI for Web Engineering Models
Web engineering faces a pressing challenge in keeping pace with the rapidly evolving digital landscape. Developing, designing, testing, and maintaining web-based systems and applications require innovative approaches to meet the growing demands of users and businesses. Generative Artificial Intelligence (AI) emerges as a transformative solution, offering advanced capabilities to enhance web engineering models and methodologies. This book presents a timely exploration of how Generative AI can revolutionize the web engineering discipline, providing insights into future challenges and societal impacts. Generative AI for Web Engineering Models offers a comprehensive examination of integrating AI-driven generative approaches into web engineering practices. It delves into methodologies, models, and the transformative impact of Generative AI on web-based systems and applications. By addressing topics such as web browser technologies, website scalability, security, and the integration of Machine Learning, this book provides a roadmap for researchers, scientists, postgraduate students, and AI enthusiasts interested in the intersection of AI and web engineering. Through insightful analysis and real-world examples, this book equips readers with the knowledge and tools to navigate the complexities of web engineering in the AI era. It offers practical strategies for leveraging Generative AI to develop more efficient, scalable, and secure web-based solutions. As the digital landscape continues to evolve, Generative AI for Web Engineering Models is a valuable resource for those seeking to stay at the forefront of web technology.
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Google Machine Learning and Generative AI for Solutions Architects
Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectivelyKey FeaturesUnderstand key concepts, from fundamentals through to complex topics, via a methodical approachBuild real-world end-to-end MLOps solutions and generative AI applications on Google CloudGet your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecyclePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer.You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learnBuild solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and SparkSource, understand, and prepare data for ML workloadsBuild, train, and deploy ML models on Google CloudCreate an effective MLOps strategy and implement MLOps workloads on Google CloudDiscover common challenges in typical AI/ML projects and get solutions from expertsExplore vector databases and their importance in Generative AI applicationsUncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflowsWho this book is forThis book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.
R 1,693.00
Generative AI in Neurology
This book comprehensively explores the intersection between AI and neurology, focusing on the application of generative models in diagnosing, treating, and managing neurodegenerative diseases. It covers the principles of generative AI and its roles in neuroimaging, biomarker identification, medication development, and tailored treatment options. It examines how advanced AI approaches are leveraged to increase the detection and treatment of neurodegenerative illnesses. The concepts are supported by case studies and examples.Features:Focuses on the emerging field of generative models in neurology.Presents an interdisciplinary approach that bridges AI and medical applications.Examines the ability of neural diffusion models to generate genuine data to enhance healthcare AI.Discusses tools for the adaptation and augmentation of medical images and healthcare data domains.Includes case studies and practical impacts.This book is aimed at graduate students and researchers in biomedical and computer engineering, artificial intelligence, and health informatics.
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Dynamic General Equilibrium Modeling
Contemporary macroeconomics is built upon microeconomic principles, with its most recent advance featuring dynamic stochastic general equilibrium models. Our textbook acquaints readers with the essential computational techniques required to tackle these models and employ them for quantitative analysis. This third edition maintains the structure of the second, dividing the content into three separate parts dedicated to representative agent models, heterogeneous agent models, and numerical methods. At the same time, every chapter has been revised and two entirely new chapters have been added.The updated content reflects the latest advances in both numerical methods and their applications in macroeconomics, spanning areas like business-cycle analysis, economic growth theory, distributional economics, monetary and fiscal policy. The two new chapters delve into advanced techniques, including higher-order perturbation, weighted residual methods, and solutions to high-dimensional nonlinear problems. In addition, we present further insights from macroeconomic theory, complemented by practical applications like the Smolyak algorithm, Gorman aggregation, rare disaster models and dynamic Laffer curves. Lastly, the new edition places special emphasis on practical implementation across various programming languages; accordingly, its accompanying web page offers examples of computer code for languages such as MATLAB®, GAUSS, Fortran, Julia and Python.
R 3,663.00
Generative AI Tools
Generative AI algorithms have become transformative forces in today's digital landscape, revolutionizing industries from healthcare and finance to creative arts and software development by enabling machines to create human-like content, automate complex tasks, and generate innovative solutions at unprecedented scale and speed. These applications are driving significant productivity gains across organizations, with AI-powered tools like large language models, image generators, and code assistants helping professionals streamline workflows, enhance creativity, and solve problems that were previously time-intensive or required specialized expertise. The strategic importance of generative AI extends beyond operational efficiency, as it represents a fundamental shift toward intelligent automation that is reshaping competitive advantages, creating new business models, and establishing AI literacy as a critical skill for future workforce success. Generative AI Tools: From Algorithms to Applications is a reference for researchers that consolidates rapidly evolving methodologies, theoretical foundations, and practical implementations. Highlights include:Generative AI tools and the marketing industryA comprehensive overview of ChatGPTChatGPT integration in IoT ecosystemsProviding a structured framework for understanding this complex and fast-moving field, the book enables researchers to build upon established knowledge more efficiently, avoid redundant work, and identify promising research directions by offering systematic coverage of current state-of-the-art techniques, their limitations, and emerging challenges that require innovative solutions. Bridging the gap between theoretical concepts and real-world applications, the book helps researchers understand not only how these algorithms work but also their practical implications, ethical considerations, and potential societal impacts, which is crucial for developing responsible and impactful AI research agendas.
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Understanding Gravity: The Generation Model Approach
Currently, the General Theory of Relativity (GTR) describes the physics of the very large in terms of classical physics, while quantum theory describes the physics of the very small in terms of the Standard Model of particle physics. Unfortunately, the two theories are incompatible and do not describe satisfactorily all the forces between the various particles comprising ordinary matter. At present, one of the deepest problems in theoretical physics is harmonizing the GTR, which describes gravitation, with quantum mechanics, which describes the other three fundamental forces acting on the atomic scale. The main aim of the book is to provide an understanding of gravity in terms of a quantum theory given by the Generation Model of particle physics. The book presents a fully quantum theory of gravity, which describes both the large cosmological scale and the small atomic scale interactions between all particles.
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Structural Pattern Recognition using Graph Matching
This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.• Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations• Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching• Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach• Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems• Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)
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Generative Methods for Social Media Analysis
This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks.The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified.The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.
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