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Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 3,438.80
Learning Generative AI Tools for Excel
Looking to take your Excel skills to the next level? With the power of AI, you can supercharge your workflows, automate repetitive tasks, and perform advanced data analysis. Written for Excel users with little to no AI experience, this book bridges the gap between traditional spreadsheet skills and the revolutionary capabilities of generative AI--empowering professionals to work smarter, faster, and with greater precision. Whether you're a data analyst, financial professional, project manager, or Excel enthusiast, this book will give you practical tutorials and actionable insights for integrating AI into your everyday work. Learn how to prepare, analyze, and visualize data using tools like ChatGPT and Copilot, and leverage AI's power to generate insights that drive better business decisions. With its step-by-step guidance, this book will be your go-to reference as you unlock a new level of productivity and analytical expertise. Learn to integrate ChatGPT and Copilot with Excel for enhanced efficiency Understand AI-driven techniques for data preparation, analysis, and visualization in Excel Discover the landscape of AI add-ins like OpenAI API and how to use them effectively Automate routine Excel tasks using generative AI for faster, more precise results Analyze data with AI tools to uncover actionable insights and drive smarter decisions
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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
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.
R 7,327.00
Handbook of Generative AI in Education
Generative AI is transforming educational systems at a pace that challenges researchers, practitioners, and institutions to develop principled approaches to understanding, evaluating, and implementing these technologies responsibly. In response to this need, this volume synthesizes research on the use of Generative Artificial Intelligence (GAI) in education from leading global scholars across disciplines. The book explores effective methodologies and frameworks for advancing research and applications of GAI in higher education and related domains while addressing new possibilities for both the research and application of teaching, learning, and assessment. It examines the ways in which GAI can be used to transform education within the context of risks, ethical concerns, and other potential negative impacts, addressing the significant new challenges GAI may pose for instructors, students, administrators and researchers.Key areas of coverage include:• Foundational frameworks for understanding GAI systems and their implications for educational research and practice• Empirical evaluation of AI tutors, agentic systems, and automated feedback against rigorous learning science standards• Innovations in assessment that expand what can be measured and at what scale• Applications and findings across K–12, higher education, health sciences, and workforce training, with attention to equity and institutional implementation• Ethical implications of GAI use in educational research and applicationsThe Handbook of Generative AI in Education is an essential resource for researchers, professors, educators, and graduate students as well as professionals and practitioners in instructional design, educational technology, educational psychology, health science education, teacher education, and educational practice and policy.
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Generative AI
The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of Phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks.The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for Phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website.But, Generative AI can also be used for the good also, to combat Phishing Attacks. This is the topic of this book. In this, we cover the following:A review of PhishingA review of AI, Neural Networks, and Machine LearningA review of Natural Language Processing, Generative AI, and the Digital PersonA proposed solution as to how Generative AI can combat Phishing Attacks, as they relate to Privileged Access accounts
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Large Language Models for Chemists
In recent years, LLMs (such as Claude, DeepSeek, Llama and other transformer-based models) have emerged as powerful tools in chemistry, enabling new approaches to scientific discovery. While many chemists, from undergraduate students to researchers find these AI models interesting, they may lack certain knowledge base to better integrate these tools into their daily research.Large Language Models for Chemists breaks down that barrier by demystifying how LLMs work in an accessible way and showing, step-by-step, how they can be applied to solve real chemistry problems. Written in a friendly, tutorial style, the book assumes only a basic background in chemistry and minimal programming experience. It begins by gently introducing artificial intelligence and machine learning concepts in lay terms, building up to the inner workings of LLMs without heavy math. Readers will learn how these models “think” and generate text, gaining an intuitive understanding of concepts like neural networks, transformers, and training data using analogies and simple diagrams. Crucially, each concept is reinforced with chemistry-focused examples – from understanding chemical nomenclature and reactions as a “language,” to exploring how an LLM can suggest synthetic routes or explain spectral data.Beyond theory, this book emphasizes practical application. Each chapter includes hands-on tutorials and case studies that invite readers to experiment with real tools. Using open-source libraries (such as RDKit for cheminformatics and standard Python machine learning frameworks), readers will walk through projects like predicting molecular properties with the aid of an LLM, generating novel compound ideas, analyzing research papers, and even using an LLM as a conversational chemistry assistant. For example, one case study guides the reader in using an LLM to mine a chemistry literature database and then write Python code to analyze reaction trends, mirroring cutting-edge research where LLMs assist in code generation and data mining for chemical discovery.
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Generative AI in Higher Education
Chan and Colloton’s book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.Since November 2022, every conversation in higher education has involved ChatGPT and its impact on all aspects of teaching and learning. The book explores the necessity of AI literacy tailored to professional contexts, assess the strengths and weaknesses of incorporating ChatGPT in curriculum design, and delve into the transformation of assessment methods in the GenAI era. The authors introduce the Six Assessment Redesign Pivotal Strategies (SARPS) and an AI Assessment Integration Framework, encouraging a learner-centric assessment model. The necessity for well-crafted AI educational policies is explored, as well as a blueprint for policy formulation in academic institutions. Technical enthusiasts are catered to with a deep dive into the mechanics behind GenAI, from the history of neural networks to the latest advances and applications of GenAI technologies.With an eye on the future of AI in education, this book will appeal to educators, students and scholars interested in the wider societal implications and the transformative role of GenAI in pedagogy and research.
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Building Generative AI Solutions for Industry with AWS
Building Generative AI Solutions for Industry with AWS is a comprehensive guide for developers, and data scientists aiming to harness the power of Generative AI across a range of industrial domains using Amazon Web Services. This hands-on book provides readers with the knowledge and practical skills to design, secure, govern, and deploy GenAI solutions effectively in real-world environments. The book begins with an introduction to the fundamentals of Generative AI and the AI/ML services offered by AWS. Readers will explore industry use cases and learn how to kick-start their journey building GenAI applications on the AWS cloud. It focuses on security and governance, covering essential practices for data privacy, access control, encryption, compliance, and ethical AI deployment. Each chapter then dives deep into specific industry applications--automotive, manufacturing, healthcare, financial services, and media & entertainment. From autonomous driving and predictive maintenance to fraud detection and content creation, readers will learn how GenAI is transforming operations and unlocking new opportunities in these fields. The book also addresses cost management strategies for GenAI on AWS, offering guidance on optimizing resources and budgeting efficiently. Concluding with future possibilities and implementation best practices, this book equips professionals with the tools and insights to build scalable, secure, and compliant GenAI systems tailored to their domain. What you will learn: Understand the fundamentals of Generative AI and AWS AI/ML services.How to build and deploy secure, compliant GenAI applications on AWS.Explore real-life GenAI use cases across industries like automotive, healthcare, and finance.Gain insights into managing GenAI costs and optimizing AWS resources. Who this book is for: Software developers, machine learning engineers, data scientists, and cloud architects who want to build real-life Generative AI solutions using AWS.
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Generative Artificial Intelligence and Ethics
The rapid advancement of generative artificial intelligence (AI) has brought about significant ethical challenges. As machines become more adept at creating human-like content, concerns about misuse, bias, privacy, and accountability have emerged. Without clear guidelines and regulations, there is a risk of unethical use, such as creating deepfake videos or disseminating misinformation, which could have severe societal consequences. Additionally, questions about intellectual property rights and the ownership of AI-generated creations still need to be solved, further complicating the ethical landscape. The book, Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices, comprehensively solves these ethical challenges. By providing insights into the historical development and key milestones of Generative AI, the book lays a foundation for understanding its complex ethical implications. It examines existing ethical frameworks and proposes new ones tailored to AI's unique characteristics, helping readers apply traditional ethics to AI development and deployment. The book equips readers with the knowledge and tools needed to address these ethical considerations in AI development through in-depth discussions on bias, fairness, privacy, and security. It also explores the role of education in fostering responsible AI practices. It offers practical guidelines for ethical decision-making throughout the AI lifecycle. By promoting ethical awareness and providing actionable solutions, this book empowers readers to navigate the moral challenges of Generative AI, ensuring its responsible and beneficial use for society.
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Generative AI for Software Development
As the field of Artificial Intelligence continues to evolve, Generative AI has emerged as a powerful tool in the realm of software development. AI models capable of generating high-quality, human-like code can significantly accelerate the software development process, assist in troubleshooting, and even automate tasks traditionally performed by human developers. Generative AI models, such as OpenAI, DeepMind, and others, are already demonstrating the ability to write functional code, generate documentation, and assist in code review. These models are not only reducing the time it takes to develop applications but are also making software development more accessible to a broader audience, including non-technical users. This book is intended to serve as a comprehensive guide for developers, technology leaders, and anyone interested in understanding how to leverage Generative AI in software development. It will cover the underlying technology behind these models, practical use cases, integration into development workflows, and ethical considerations. Additionally, it explores how the democratization of coding through AI will shape the future of software engineering. By providing insights into real-world applications and best practices for integrating generative AI into the software lifecycle, this book empowers readers to stay ahead of the technological curve in a rapidly evolving industry.
R 6,380.00
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 Higher Education:Guiding Principles for Teaching and Learning
Divided into two volumes, this book develops guiding principles for higher education institutions to use GenAI effectively and ethically in teaching and learning, articulating a roadmap for implementation at institutional levels and addressing the conundrum of using GenAI in higher education. As higher education institutions take different attitudes and approaches to Generative AI (GenAI), with some viewing it as a threat to academic integrity and therefore implementing a policy of banning its use, while others have embraced it as an innovation to academic practice and have implemented guidance on how to use it ethically, this book makes clear that GenAI , such as ChatGPT, is not the problem itself, but rather the issue is how we engage with it.
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Generative AI for Second Language Writing Feedback, Assessment, and Instruction
Generative AI (GenAI) technology is transforming the landscape of language teaching and learning and has attracted considerable attention from researchers and educators in the field of second language (L2) education. Research has shown that, when used appropriately, GenAI can support students throughout the writing process, provide high-quality feedback on written work, and facilitate the assessment of L2 writing. This Element presents five innovative topics that the co-authors have explored: (1) student-GenAI interaction during the writing process; (2) collaborative processing of GenAI-generated feedback; (3) GenAI-supported teacher feedback; (4) the potential of GenAI for L2 writing assessment; and (5) teacher education for the effective integration of GenAI in L2 writing instruction. By synthesizing current research and practical applications, this Element aims to inspire researchers, practitioners, and graduate students to further investigate the role of GenAI in L2 writing contexts.
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Pioneering Applications of Generative AI
Integrating generative artificial intelligence (AI) into art, design, and media presents a double-edged sword. While it offers unprecedented creative possibilities, it raises ethical concerns, challenges traditional workflows, and requires careful regulation. As AI becomes more prevalent in these fields, there is a pressing need for a comprehensive resource that explores the technology's potential and navigates the complex landscape of its implications. The Pioneering Applications of Generative AI is a pioneering book that addresses these challenges head-on. It provides a deep dive into the evolution, ethical considerations, core technologies, and creative applications of generative AI, offering readers a thorough understanding of this transformative technology. By showcasing cutting-edge research and innovative use cases, the book bridges academia and practice, fostering cross-disciplinary collaboration and pushing the boundaries of creativity. Researchers, academicians, scientists, and research scholars will find this book invaluable in navigating the complexities of generative AI in art, design, and media. With its focus on ethical and responsible AI and discussions on regulatory frameworks, the book equips readers with the knowledge and tools needed to harness the full potential of generative AI while ensuring its responsible and ethical use.
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Generative AI 2.0 and Data Analytics
Data analytics and generative AI (Gen AI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.Generative AI 2.0 and Data Analytics explores the intersection between Gen AI and data analytics and addresses its profound efects on industries and organizations across the globe. Highlights of the book include:Deep learning architectures for generative models in business data managementOptimizing human-AI collaboration for strategic decision-making in business practisesBenchmarking practices and evaluation metrics for Generative AI in business data analyticsNot only covering the fundamental concepts, and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.
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Practical Guide to Using Generative AI in Photoshop
Your guide to the world of Generative AI in Photoshop! Everyone s talking about the new Generative AI capabilities of Adobe Photoshop, and for good reason: This is the biggest update to Photoshop in its history. With the power of AI photo editing, you can now remove, expand, generate, change, or combine almost anything you need to in order to create the images you want. While these features are incredibly powerful, it can be a struggle to get the best results from these powerful tools. What exactly can Generative Fill and Generative Expand do, and how do they do it? When should you use these tools, and when should you use another tool? If you want to incorporate these tools into your workflow, it s very important to get an early grasp of these tools now and get ahead of the crowd. In this book, bestselling author Colin Smith walks you through Generative AI in Photoshop. With decades of experience working in the real world and training for Adobe, Apple, Nvidia, and many others, he knows Photoshop on a level that few do. He also has a knack for making learning fun and easy, and he can take complex topics and make them seem simple. You will learn to: Generate a scene from scratch Extend an image Fix panoramas Remove crowds Remove any object Generate objects Replace objects Duplicate and move an image Combine variations of generated images Blend images into an extended scene Repair old or damaged photos Composite with your own photos Composite your photo into an AI scene Change backgrounds Add reflections Generate depth of field Create actions with Generative AI commands And much more! Make no mistake: Generative AI is here to stay, and it is revolutionizing how we work in Photoshop. With The Practical Guide to Using Generative AI in Photoshop by your side, you will be ready to confidently move forward with these new tools and apply them to your own imagery. Whether you want to simply clean up your photos or create fantastical works of art or anything in between your only limitation will be your imagination.
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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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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
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Revolutionizing Healthcare 5.0: The Power of Generative AI
This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.
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Using Generative AI Effectively in Higher Education
Using Generative AI Effectively in Higher Education explores how higher education providers can realise their role and responsibility in harnessing the power of generative artificial intelligence (GenAI) ethically and sustainably.This rich collection of established and evaluated practices from across global higher education offers a practical guide to leading an agile institutional response to emerging technologies, building critical digital literacy across an entire institution, and embedding the ethical and sustainable use of GenAI in teaching, learning, and assessment. Including reflections from stakeholders testifying to the value of the approaches outlined, the book examines how higher education can equip staff and students with the critical-digital literacy necessary to use GenAI in work, study, and social life responsibly and with integrity. It provides an evidence-based resource for any kind of higher education (HE) provider (modern, college-based, and research-focused) looking for inspiration and approaches which can build GenAI capability and includes chapters on the development of cross-institutional strategy, policies and processes, pedagogic practices, and critical-digital literacy.This resource will be invaluable to educational leaders, educational developers, learning developers, learning technologists, course administrators, quality assurance staff, and HE teachers wishing to embrace and adapt to a GenAI-enabled world.
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Advanced Applications of Generative AI and Natural Language Processing Models
The rapid advancement of technology in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and generative AI, presents a challenge for academic scholars to remain informed with the most current information and with the latest techniques and applications. The lack of comprehensive resources also hinders researchers' ability to gain a deep understanding of these subjects and effectively implement these technologies. Advanced Applications of Generative AI and Natural Language Processing Models is a comprehensive resource which offers an effective solution by investigating cutting-edge developments in NLP and generative AI. It provides insights into how these technologies function, their benefits, and the associated challenges.This book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI for students, researchers, and professionals in AI, NLP, and computer science. It uses real-world examples and practical applications to empower scholars to apply their knowledge and solve complex problems.Advanced Applications of Generative AI and Natural Language Processing Models is a complete guide that equips researchers with the knowledge needed to utilize NLP and generative AI, including deep learning for NLP, generative models for text generation, language modelling and sentiment analysis, machine translation and multilingual models, ethical considerations in NLP, conversational AI applications, and case studies and real-world applications.
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Generative AI Application Integration Patterns
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.What you will learnConcepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design patternsProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights
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Generative Artificial Intelligence in Literacy Education
Artificial Intelligence is rapidly reshaping education, assisting teachers in planning, resource creation, and assessment while students increasingly engage with AI for learning and text construction. Despite restrictions on GenAI use in schools, its presence is undeniable, prompting urgent questions about its role in literacy education.Since the introduction of ChatGPT in 2023, research has surged on AI’s impact on higher education and foreign language instruction. However, the intersection of GenAI and literacy education for school-aged students (K-12) remains underexplored. This essential volume examines AI-assisted learning, student engagement with AI-generated texts, and the implications for literacy educators as GenAI becomes more powerful and accessible. Furthermore, it explores the evolving responsibilities of teachers as AI supports professional learning, planning, and assessment.With GenAI’s dynamic and expanding capabilities, Generative Artificial Intelligence in Literacy Education offers timely insights into the future of literacy education. A must-read for educators, researchers, and policymakers seeking to navigate the complex relationship between artificial intelligence and the teaching of reading and writing in K-12 schools.
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Generative and Open AI in Industry 5.0
This book provides an up-to-date and easy-to-read introduction to Generative AI and its revolutionary contribution to building Industry 5.0. Written for both practitioners and interested readers, it is a clear and compelling overview of the technologies at the heart of the next industrial revolution.Part one introduces the conceptual notions behind generative models, large language models (LLMs), and the neural networks that drive them. It delves into the mathematical and conceptual foundations of Generative AI, transformer architecture, and the practical applications of language and vision models. The second half links the innovations to Industry 5.0, discussing digital twins, predictive maintenance, DevOps, and cybersecurity in intelligent systems. It encompasses real-world applications of AI in areas such as finance, manufacturing, and smart infrastructure. Case studies, like the application of generative models in FinTech and blockchain adoption, underscore how AI is already transforming business and society around the world.The book is perfect for students, researchers, and practitioners of AI, data science, and industrial systems interested in comprehending the theory and practical applications of Generative AI. It also benefits educators and policy makers who wish to lead in cutting-edge technological advancements, with emphasis on responsible, secure, and human-oriented AI applications for a brighter future.
R 5,165.00
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
R 1,482.00
