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Guide to Using Generative AI in Programming
Generative AI has changed the landscape of programming. How can AI tools be used effectively and safely, and what is the role of human programmers now? This unique textbook/guide is a comprehensive introduction to using generative AI in programming. Based on both practical experience and scientific studies, it shows how AI tools can be applied effectively to programmers' tasks--such as information retrieval, code generation, test creation, and code analysis. The book explores both the new possibilities and the risks of using generative AI, emphasizing the importance of programmers' expertise: Whereas AI tools can make programmers more productive, their use may also lead to serious problems. The book also addresses how learning and teaching programming are evolving in the era of generative AI. Topics and features: Explains how large language models work under the hood, and how this affects their abilities and limitationsIncludes a wealth of practical examples of using AI tools in a variety of programming languagesOffers advice on how to build effective prompts for tasks such as generating professional code and learning new programming languagesExplores recent scientific literature on using generative AI in software development and programming educationShows how generative AI connects to the history of programming, including developments in programming paradigms and abstractions This textbook is an ideal guide for students and programmers wishing to understand the role of generative AI in programming, as well as to learn both how to use AI tools in practice and the theory behind them. The book assumes basic programming knowledge, but no prior experience with AI tools is necessary. It is suitable both for beginners learning programming and for experienced programmers looking to update their skills.
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Using Generative AI in Historical Practice
'Using Generative AI in Historical Practice' argues that generative models are reshaping historical scholarship. Rejecting medium - and long -term speculation, it focuses on near-term practice: how historians can use AI now to augment their research through context-aware dialogue, semantic search, network visualization, multimodal source analysis, and code-assisted workflows. It details methods for context management, task design, and response structure, while warning against cognitive offloading and model bias. While it offers a variety of novel methodologies, the book insists on the indispensability of human agency and taste. Case studies range from Augustine of Hippo to early cinematography, demonstrating the possibilities and limits of generative AI. It concludes with a call to historians to engage with the technology critically and productively, reimagining AI-assisted scholarship without surrendering disciplinary standards and aims.
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Generative AI for Cloud Solutions
Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key FeaturesGain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloudUnderstand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AIPeek into the future to explore emerging trends like multimodal AI and autonomous agentsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms.This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements.By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.What you will learnGet started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function togetherUnderstand how we started applying NLP to concepts like transformersGrasp the process of fine-tuning and developing apps based on RAGExplore effective prompt engineering strategiesAcquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIsDiscover how to scale and secure GenAI systems, while understanding the principles of responsible AIWho this book is forThis artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book.
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Generative Systems and Intelligent Tutoring Systems
This book constitutes the refereed proceedings of the 21st International Conference on Intelligent Tutoring Systems, ITS 2025, held in Alexandroupolis, Greece, during June 2-6, 2025. The 21 full papers, 27 short papers and 5 posters included in this book were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical conference tracks: Part I: Generative Tutoring Systems. The goal of this part is to show how new techniques inspired by artificial intelligence (AI) and new methods in education can improve learning, teaching, and generate the capacity for knowledge acquisition and much more. Part II: Application areas, environments, and techniques for AI systems. This part shows the progress of research investigating the different application areas (such as education, health), techniques (such as neural networks, data mining, natural language processing) and environments (such as games, virtual reality, cognitive robots) for effective AI systems.
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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
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Teaching and Learning in the Age of Generative AI
Winner of the 2025 Systems Thinking & Change Division Outstanding Book Award from the Association for Educational Communications and Technology!Teaching and Learning in the Age of Generative AI explores how educators can effectively harness the potential of artificial intelligence technologies while skillfully navigating its pedagogical, technical, ethical, institutional, and societal implications. The increasing accessibility of AI technologies among K-12 and higher education students has raised extensive concerns around academic integrity, though a deeper lineage of research and development suggests that these tools may be used to supplement instruction, prioritize critical thinking, and promote digital literacy. Bookended by in-depth analyses of the historical and future trajectories of artificial intelligence in education, this comprehensive resource provides evidence-based strategies for classroom implementation and helpful summaries of common benefits and risks. Teaching assistance, personalized learning, redefined assessments, anti-bias measures, and safeguards against misconduct and privacy infringement are among the wealth of topics addressed in these chapters. This book is an ideal text for undergraduate and graduate students of teacher education and curriculum and instruction as well as for higher education teaching faculty, school technology coordinators, and talent development personnel in training and in service.
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Generative Artificial Intelligence and Language Teaching
The development of Generative Artificial Intelligence (GenAI) has led to intense wonder, surprise, excitement, and concern within the language teaching profession. These tools offer the potential to assist language teachers in helping their learners achieve their language learning goals, and at the same time, risk disrupting language teaching and learning processes, the teaching profession, and possibly the instrumental needs to learn foreign languages. This Element provides an accessible introduction and guide to the use of GenAI for language teaching. It aims to facilitate language teachers' development of the professional knowledge and skills they need to use GenAI responsibly, ethically and effectively. The Element It is a valuable resource for pre-service and in-service language teachers of all experience levels. Each section includes helpful tips and questions for reflection to get teachers started with GenAI while ensuring they engage critically and responsibly with these tools. Evidence-informed approaches are promoted throughout the Element.
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AI-Generated Image and Video Synthesis
Technical depth and ethical frameworks for AI visual media synthesis Generative AI models for visual media are transforming virtual reality and biomedical imaging while raising urgent questions about deepfakes and misinformation. AI-Generated Image and Video Synthesis addresses both dimensions. A team of researchers provide algorithmic foundations alongside detection strategies, authentication methods, and regulatory analysis. Coverage spans text-to-image generation, image-to-image translation, video synthesis, neural rendering, and 3D-aware generation. The book examines AI applications in CT, MRI synthetic data augmentation, and virtual staining for biomedical contexts. Case studies explore AI-assisted filmmaking, music videos, and style transfer. A dedicated chapter forecasts emerging trends including diffusion-transformer hybrids and autonomous generative agents. Readers will also find: Comparative analyses of generative models including GANs, diffusion models, and transformers with implementation guidance and code repositories for hands-on experimentationDeepfake detection strategies and digital content authentication techniques addressing misinformation, intellectual property rights, and emerging regulatory frameworks worldwideIndustry case studies demonstrating real-world deployments in creative industries, surveillance systems, education, and cultural preservation applicationsBiomedical imaging applications covering synthetic data generation for CT and MRI, virtual staining techniques, and data augmentation strategiesPractical toolkits and online resources supporting implementation and evaluation of AI synthesis techniques across professional and academic contexts Designed for AI researchers, computer vision engineers, and graduate students studying deep learning and image processing, this book connects theoretical principles with practical deployment. The combination of technical depth, application coverage, and ethical analysis makes it a comprehensive resource for professionals navigating AI-generated visual media.
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Generative Systems and Intelligent Tutoring Systems
This book constitutes the refereed proceedings of the 21st International Conference on Intelligent Tutoring Systems, ITS 2025, held in Alexandroupolis, Greece, during June 2-6, 2025. The 21 full papers, 27 short papers and 5 posters included in this book were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical conference tracks: Part I: Generative Tutoring Systems. The goal of this part is to show how new techniques inspired by artificial intelligence (AI) and new methods in education can improve learning, teaching, and generate the capacity for knowledge acquisition and much more. Part II: Application areas, environments, and techniques for AI systems. This part shows the progress of research investigating the different application areas (such as education, health), techniques (such as neural networks, data mining, natural language processing) and environments (such as games, virtual reality, cognitive robots) for effective AI systems.
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GPT Meets Game Theory
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
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How to Use Generative AI in Educational Research
Artificial Intelligence technologies have impacted our world in ways we could not have imagined a decade ago. Generative AI (GenAI), a powerful, complex and general use subset of AI has become available to the public in recent years. GenAI's effect on education, research, and academic practice is far-reaching and exciting, yet also deeply concerning. While GenAI has the potential to offer transformation in the practice of educational research, there are few resources which clarify why, when, and how these tools might be used ethically and sensitively. This Element introduces key areas of consideration for education researchers seeking to use GenAI, including examining the existing research, critically evaluating the benefits and risks of GenAI in educational research, and providing example use-cases of good and bad practice.
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Little Guide for Teachers: Generative AI in the Classroom
Generative AI has the potential to transform teaching by reducing workload, enhancing learning, and fostering creativity. It also poses significant challenges and raises important questions. This book is for teachers who want to know more about generative AI: how it works, the ethical questions it raises and what it can do for them and their students.The Little Guide for Teachers series is little in size but BIG on all the support and inspiration you need to navigate your day-to-day life as a teacher.*Authored by experts in the field*Easy to dip in-and-out of*Interactive activities encourage you to write into the book and make it your own*Read in an afternoon or take as long as you like with it!
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Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers
The integration of generative AI and deep learning techniques for Alzheimer's disease detection significantly impacts the research community by advancing diagnostic accuracy and providing a comprehensive understanding of the disease. By combining multiple data modalities, including imaging, genetics, and clinical data, researchers can improve diagnostic precision and develop personalized treatment strategies. Generative AI facilitates efficient data utilization through dataset augmentation, fostering innovation and collaboration across interdisciplinary fields. These methodologies forward the exploration of new diagnostic tools while expediting their application in clinical practice, benefiting patients through early detection and intervention. The incorporation of generative AI may enhance research capabilities, promote collaboration, and improve Alzheimer's disease management and patient outcomes. Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers explores the integration of deep generative models in disease diagnosis, biomarking, and prediction. It examines the use of tools like data analysis, natural language processing, and machine learning for effective Alzheimer's research. This book covers topics such as data analysis, biomedicine, and machine learning, and is a useful resource for computer engineers, biologists, scientists, medical professionals, healthcare workers, academicians, and researchers.
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Generative Artificial Intelligence and Creativity
Generative Artificial Intelligence and Creativity: Possibilities, Precautions, and Perspectives explores the dynamic relationship between generative AI (genAI) and creativity. This book brings together global scholars from diverse fields to explore the transformative impact of genAI on human creativity. As technological advancements in genAI continue to accelerate, this book navigates through the complexities of understanding and harnessing its potential. Generative Artificial Intelligence and Creativity not only dissects the fundamental principles underlying AI's contribution to creativity but also addresses the paradoxical impact of AI on human creative industries. This book serves as a unique guide for anyone seeking to comprehend the implications of AI on the future of human creativity.
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Machine Learning and Generative AI in Smart Healthcare
The healthcare landscape is constantly evolving, and one of the most significant concerns that healthcare professionals deal with is understanding how to use biomedical intelligence to improve patient outcomes. With the increasing complexity of healthcare computing systems, including technologies like deep learning and the Internet of Things, it can be challenging to navigate these advancements. Machine Learning and Generative AI in Smart Healthcare is a practical tool for healthcare professionals, researchers, and policymakers who are seeking to implement biomedical intelligence solutions. It provides a clear roadmap for using prescriptive and predictive analytics in machine learning to enhance healthcare outcomes. Going beyond the basics, it delves into healthcare computing and networking complexities. By delving into topics such as data mining, disease prediction, and AI applications, deep learning approaches, decision support systems, and optimization techniques, this book equips readers with the practical knowledge they need to optimize healthcare delivery and management.
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Generative Machine Learning Models in Medical Image Computing
Generative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics.Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing.
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Generative Artificial Intelligence and Language Teaching
The development of Generative Artificial Intelligence (GenAI) has led to intense wonder, surprise, excitement, and concern within the language teaching profession. These tools offer the potential to assist language teachers in helping their learners achieve their language learning goals, and at the same time, risk disrupting language teaching and learning processes, the teaching profession, and possibly the instrumental needs to learn foreign languages. This Element provides an accessible introduction and guide to the use of GenAI for language teaching. It aims to facilitate language teachers' development of the professional knowledge and skills they need to use GenAI responsibly, ethically and effectively. The Element It is a valuable resource for pre-service and in-service language teachers of all experience levels. Each section includes helpful tips and questions for reflection to get teachers started with GenAI while ensuring they engage critically and responsibly with these tools. Evidence-informed approaches are promoted throughout the Element.
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Video Generation with AI
Video generation is rapidly becoming a key area in generative AI—combining spatial, temporal, and multimodal reasoning to produce moving images that are both coherent and creative. For many practitioners, however, understanding how these models function and implementing them remains a significant challenge. Video Generation with AI offers a straightforward guide for exploring this new terrain.Author Joseph Enochs leverages his experience leading enterprise AI projects to clarify how diffusion transformers, multimodal large language models, and spatiotemporal architectures combine to create high-quality video. Blending technical detail with practical examples, he demonstrates how to transition from isolated experiments to production-ready systems that transform creative fields, media, and human-machine collaboration.Understand the fundamental architectures of modern video generative modelsTrain and fine-tune models using diffusion transformers and multimodal encodersMaintain temporal coherence and consistency across complex scenes and sequencesApply generative video tools to creative, industrial, and scientific workflowsEvaluate, troubleshoot, and improve generated video quality at scale
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Generative AI and Sustainability in Higher Education
The integration of generative AI into higher education presents a unique opportunity to enhance sustainability. AI-driven solutions can optimize resources and processes and provide tools to tap into the potential of generative AI in creating more personalized, efficient, and sustainable educational environments. This book discusses the role of generative AI in higher education and provides a clear vision of how to integrate AI technology within the framework of sustainability. It balances theoretical insights with practical applications and ethical considerations. Readers will learn how to navigate the ethical and social implications, understand sustainability initiatives, and foresee future trends. FeaturesProvides a holistic explanation of complex AI concepts for readers without technical backgrounds.Includes detailed real-world case studies and practical examples showcasing generative AI's impact on education.Incorporates sustainability and highlights eco-friendly approaches and sustainable practices in educational settings.Offers insights into the ethical considerations and responsible use of AI in education.Forecasts future trends and advancements, helping educators stay ahead in the fast-evolving educational landscape.This is an excellent resource for everyone interested in implementing AI technologies in higher education to enhance students’ learning experience and outcomes and for all readers who are interested in the latest trends and applications of AI in higher education.
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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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Generative AI in Software Development
Generative AI in Software Development: Practical Ways AI is Building the Future of Code explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn’t a theoretical or academic deep dive it’s a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer. What sets this book apart is its balance: it's not just for hardcore engineers, nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful, but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's becoming essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.
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Generative Adversarial Networks Projects
Explore various Generative Adversarial Network architectures using the Python ecosystemKey FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook DescriptionGenerative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation.By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is forIf you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
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