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RAG-Driven Generative AI
Building MAS-RAG (multi-agent AI systems for RAG) that reason over real-world data using hybrid retrieval and scalable architectures for production use.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster DualRAG by combining vector search with SQL filtering over structured enterprise dataImplement GraphRAG, Spatial-RAG, and vector search natively in Oracle Database 23aiBuild multimodal video pipelines with human-feedback loops and fine-tuned modelsBook DescriptionStop moving your data to the AI. This second edition defines a revolutionary architectural shift: bringing the AI to the data. By using Oracle Database 23ai as a converged engine in this book, you will architect Sovereign AI systems that eliminate the fragmentation, latency, and massive security risks inherent in traditional data extraction.You’ll work with DualRAG, synchronizing unstructured vector semantics with the deterministic truth of structured SQL, Graph, and Spatial retrieval. This allows your systems to reason over verified corporate data rather than probabilistic guesses, reducing hallucinations at the source. Moving beyond simple pipelines, you’ll also build MAS-RAG (multi-agent systems for RAG), where autonomous agents coordinate across hybrid retrieval workflows, multimodal video pipelines, and graph-based knowledge structures.Designed for developers and architects, these blueprints transform disconnected data silos into a unified engine to architect autonomous enterprise intelligence that scales with RLHF and model fine-tuning. By the end of the book, you’ll be able to design and deploy enterprise AI systems that combine retrieval, reasoning, and structured data to build reliable generative AI applications.*Email sign-up and proof of purchase requiredWhat you will learnBring intelligence directly to the data within Oracle Database 23aiDefeat hallucinations and data poisoning with DualRAG, synchronizing vector semantics with structured SQLBuild MAS-RAG pipelines with Planner, Agent Registry, and MCP-standardized sovereign agentsEngineer an inference-time router using hybrid adaptive RAG to switch between reasoning, retrieval, and human feedbackFuse vector similarity, Oracle Spatial, and SQL Property Graph traversal into a converged hyper-queryMultimodal video RAG with version-controlled schema registry and semantic vector search over visual assetsWho this book is forThis book is for AI engineers, ML engineers, data scientists, and MLOps professionals who want to build production-ready generative AI systems grounded in enterprise data. It will also benefit solutions architects, database engineers, and software developers looking to integrate large language models with structured and unstructured data sources using modern retrieval architectures. Readers should be comfortable with Python and have a basic understanding of machine learning concepts. Prior experience with generative AI or vector databases will help you get the most ou
R 1,791.00
Introduction to Generative AI with Julia and Python
Elevate your coding capabilities and gain the skills necessary for maximized performance and career opportunities in the field of Artificial Intelligence. This book is a step-by-step guide designed to equip software engineers with solid foundation of Generative AI basics. The book begins by introducing Generative AI using Google Colab to establish a solid foundation, before demonstrating how to leverage Python and Julia's AI capabilities to construct Artificial Neural Networks. You'll then explore advanced AI models, such as variational autoencoders, in order to generative images and text. Author Pierluigi Riti not only navigates the complexities of this emerging topic but fills a crucial gap by guiding software engineers through theory before leading them to produce different types of models using only free resources. What You Will Learn Acquire a solid foundation of Generative AI basics.Utilize the capabilities of Python and Julia to constructure Artificial Neural NetworksExplore advanced AI models including variational autoencoders, to generate images Survey different architectures for AI and utilize free resources to produce different AI Who This Book Is For Programmers new to artificial intelligence, but with a fundamental grasp of Python.
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R 611.00
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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Generative AI for Business Innovation
This book is a comprehensive guide for business leaders, CXOs, and technical architects looking to adopt Generative AI technologies to create innovative applications for their organizations and clients. The book is divided into three parts. In Part 1, readers will learn the fundamental concepts of Generative AI, such as foundational models and large language models like ChatGPT. It provides an overview of various Generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This section helps leaders understand the benefits and risks associated with Generative AI, supported by industry case studies showcasing real-world applications. Part 2 focuses on developing a winning strategy for implementing Generative AI. It discusses choosing the right approach, building the necessary architecture and infrastructure, and ensuring data quality for training models. It also addresses technical challenges like computing power and offers best practices for execution. In Part 3, the book explores best practices for adopting Generative AI, emphasizing governance, talent development, and continuous improvement. It also discusses emerging trends and the societal impact of Generative AI, particularly concerning sustainability. By the end, readers will be equipped with the knowledge and strategies to effectively harness Generative AI, paving the way for innovation in their organizations. What you will learn: What are the various Generative AI technologies - Foundational models, LLM, Auto encoders, GANs, VAEs, RAG, prompt engineering and more.How to build a successful strategy for architecting and implementing Generative AIUnderstand Preprocessing and setting up quality data for training generative AI modelsWhat are the future trends in generative AI to build sustainable and trustworthy solutions Who this book is for: Individuals in leadership positions, such as CEOs, CTOs, CIOs, and other senior executives, who are responsible for driving innovation and strategy. decision-making in their organizations and interested in adopting Gen AI for that purpose. Professionals who design and implement technical solutions and infrastructure using Generative AI and machine learning systems.
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Generative AI and LLMs
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
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Generative AI, Cybersecurity, and Ethics
"Generative AI, Cybersecurity, and Ethics is an essential guide for students, providing clear explanations and practical insights into the integration of generative AI in cybersecurity. This book is a valuable resource for anyone looking to build a strong foundation in these interconnected fields."—Dr. Peter Sandborn, Professor, Department of Mechanical Engineering, University of Maryland, College Park "Unchecked cyber-warfare made exponentially more disruptive by Generative AI is nightmare fuel for this and future generations. Dr. Islam plumbs the depth of Generative AI and ethics through the lens of a technology practitioner and recognized AI academician, energized by the moral conscience of an ethical man and a caring humanitarian. This book is a timely primer and required reading for all those concerned about accountability and establishing guardrails for the rapidly developing field of AI."—David Pere, (Retired Colonel, United States Marine Corps) CEO & President, Blue Force Cyber Inc. Equips readers with the skills and insights necessary to succeed in the rapidly evolving landscape of Generative AI and cyber threats Generative AI (GenAI) is driving unprecedented advances in threat detection, risk analysis, and response strategies. However, GenAI technologies such as ChatGPT and advanced deepfake creation also pose unique challenges. As GenAI continues to evolve, governments and private organizations around the world need to implement ethical and regulatory policies tailored to AI and cybersecurity. Generative AI, Cybersecurity, and Ethics provides concise yet thorough insights into the dual role artificial intelligence plays in both enabling and safeguarding against cyber threats. Presented in an engaging and approachable style, this timely book explores critical aspects of the intersection of AI and cybersecurity while emphasizing responsible development and application. Reader-friendly chapters explain the principles, advancements, and challenges of specific domains within AI, such as machine learning (ML), deep learning (DL), generative AI, data privacy and protection, the need for ethical and responsible human oversight in AI systems, and more. Incorporating numerous real-world examples and case studies that connect theoretical concepts with practical applications, Generative AI, Cybersecurity, and Ethics: Explains the various types of cybersecurity and describes how GenAI concepts are implemented to safeguard data and systemsHighlights the ethical challenges encountered in cybersecurity and the importance of human intervention and judgment in GenAIDescribes key aspects of human-centric AI design, including purpose limitation, impact assessment, societal and cultural sensitivity, and interdisciplinary researchCovers the financial, legal, and regulatory implications of maintaining robust security measuresDiscusses the future trajectory of GenAI and emerging challenges such as data privacy, consent, and accountability Blending theoretical
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Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
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Learning with Generative Artificial Intelligence
This book delves into the core of education’s digital transformation, presenting a thorough and empirical examination of generative artificial intelligence (GenAI)’s impact beyond the theoretical and fragmented insights prevalent in current discourse.Drawing from peer-reviewed and extensive empirical studies, the contributors aim to unveil the multifaceted effects of GenAI (particularly ChatGPT) on learning. They navigate through topics of interaction, assessment, emotion, effect and efficiency, meta-cognition, and ethics, offering a comprehensive exploration of GenAI’s educational implications. This book presents a closed loop of learning theory, multimodal data, and learning analytics technology. Furthermore, this book builds and proposes core conceptual models for future learning and identifies potential research directions.This book will serve as a foundational reference for educators seeking innovative learning and teaching methods and for researchers and technologists who seek to push the boundaries of educational technology and related areas.
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Systemic Text Generation as Problem Solving
This study explores an approach to text generation that interprets systemic grammar as a computational representation. Terry Patten demonstrates that systemic grammar can be easily and automatically translated into current AI knowledge representations and efficiently processed by the same knowledge-based techniques currently exploited by expert systems. Thus the fundamental methodological problem of interfacing specialized computational representations with equally specialized linguistic representations can be resolved. The study provides a detailed discussion of a substantial implementation involving a relatively large systemic grammar, and a formal model of the method. It represents a fundamental and productive contribution to the literature on text generation.
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Time Series Forecasting Using Generative AI
"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies."The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.Who this book is for:Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
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Generative AI in the English Composition Classroom
Designed to cater to the needs of both novice and seasoned writing instructors, this book provides a range of practical and adaptable strategies for integrating generative artificial intelligence (AI) into English writing curricula.Generative AI in the English Composition Classroom proposes strategic methodologies to ensure that AI is utilized as a facilitator of learning and creativity, rather than as a shortcut to academic success. With a particular emphasis on sophisticated large language models such as Claude, ChatGPT, and Gemini, this book critically addresses potential challenges, including concerns related to academic integrity. It includes case studies and practical strategies to exemplify how AI can enhance the writing process while emphasizing the continuing importance of a solid foundation in writing structure, processes, and rhetorical strategies. These case studies and strategies are designed for immediate application, offering educators and students practical tools to effectively navigate AI-augmented writing environments. Finally, the book looks to the future, discussing the evolving skillsets required in the workforce and how educators can equip students for a future in which AI is an integral component.A forward-thinking and invaluable guide, this book will be of interest to educators involved in teaching English Composition and writing.
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Generative KI für Dummies
Die Möglichkeiten generativer KI optimal nutzen Mithilfe von generativer Künstlicher Intelligenz lassen sich Texte, Bilder, Videos, Sprache, Musik, Softwarecodes und auch Kunst und Design erschaffen. Doch wie können Sie die Möglichkeiten generativer KI individuell am besten nutzen? KI-Expertin Pam Baker gibt Ihnen einen guten Überblick und führt Sie in die bekanntesten Tools ein. Das Buch erklärt Ihnen, wie Sie geeignete Plattformen finden, gute Prompts schreiben, KI in Ihren Workflow einbinden und gezielt einsetzen – und entscheiden, wann doch ein wenig Human Touch gebraucht wird. Gut verständlich und praxisnah: der ideale Einstieg in ein Thema, das unsere Arbeitswelt rasant verändert. Sie erfahren Welche Plattformen sich für welche Zwecke eignenWie Sie KI-generierte Texte, Bilder, Videos und Audios erstellenWie Sie KI in Ihren Workflow integrierenWie es Ihnen gelingt, KI-Inhalte »menschlicher« zu machen
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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 the English Composition Classroom
Designed to cater to the needs of both novice and seasoned writing instructors, this book provides a range of practical and adaptable strategies for integrating generative AI into English writing curricula.Generative AI in the English Composition Classroom proposes strategic methodologies to ensure that AI is utilized as a facilitator of learning and creativity, rather than as a shortcut to academic success. With a particular emphasis on sophisticated large language models such as Claude, ChatGPT, and Gemini, this book critically addresses potential challenges, including concerns related to academic integrity. It includes case studies and practical strategies to exemplify how AI can enhance the writing process while emphasizing the continuing importance of a solid foundation in writing structure, processes, and rhetorical strategies. These case studies and strategies are designed for immediate application, offering educators and students practical tools to effectively navigate AI-augmented writing environments. Finally, the book looks to the future, discussing the evolving skillsets required in the workforce and how educators can equip students for a future in which AI is an integral component.A forward-thinking and invaluable guide, this book will be of interest to educators involved in teaching English composition and writing.
R 3,599.86
Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices
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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Building Generative AI Services with Fastapi
Ready to build production-grade applications with generative AI? This practical guide takes you through designing and deploying AI services using the FastAPI web framework. Learn how to integrate models that process text, images, audio, and video while seamlessly interacting with databases, filesystems, websites, and APIs. Whether you're a web developer, data scientist, or DevOps engineer, this book equips you with the tools to build scalable, real-time AI applications.Author Alireza Parandeh provides clear explanations and hands-on examples covering authentication, concurrency, caching, and retrieval-augmented generation (RAG) with vector databases. You'll also explore best practices for testing AI outputs, optimizing performance, and securing microservices. With containerized deployment using Docker, you'll be ready to launch AI-powered applications confidently in the cloud.Build generative AI services that interact with databases, filesystems, websites, and APIsManage concurrency in AI workloads and handle long-running tasksStream AI-generated outputs in real time via WebSocket and server-sent eventsSecure services with authentication, content filtering, throttling, and rate limitingOptimize AI performance with caching, batch processing, and fine-tuning techniques
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Deep Swarm and Evolution for Generative Artificial Intelligence
This book provides theoretical and practical knowledge about swarm and evolutionary approach of generative AI and Large Language Models (LLMs). The development of such tools contributes to better optimizing methodologies with the integration of several machinelearning and deep learning techniques. In particular, it discusses how the “emergence” concept can contribute to the improvement of AI.The book aims to model human cognitive f unction in terms of “emergence” and to explain the feasibility of AI. To this end, it focuses on human perceptions of “utility.” It describes the emergence of various cognitive errors, and irrational behaviours in the multiobjective situations. It also reviews the cognitive differences and similarities between humans and LLMs. Such studies are important when applying LLMs to real-world tasks that involve human cognition, e.g., financial engineering and market issues.The book describes the intelligent behaviour of living organisms. This is to clarify how to achieve AI in the direction of artificial life. It describes sexual selection, which is a well-known natural phenomenon that troubled Darwin, i.e., why evolutionarily useless items evolved such as peacock feathers and moose antlers etc. The book shows how sexual selection is extended as “novelty search” for the application of generative AI, i.e., the image generation with diffusion model. Real-world applications are emphasised. Empirical examples from real-world data show how the concept of deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, e-commerce Web Shop and image generation etc.
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Generative AI at AWS
Bridge business goals and technical execution to build, deploy, and govern generative AI solutions on AWSKey FeaturesAlign business strategy with practical generative AI use cases on AWSBuild MVPs, agents, and production systems with Bedrock and SageMakerApply governance, scaling, and responsible AI practices across industriesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionCut through the noise around generative AI and learn how to turn promising ideas into secure, scalable solutions on AWS. This book helps you connect business priorities with technical execution, so you can identify worthwhile use cases, select the right models and services, and move from pilot to production with confidence.You explore the fundamentals of generative AI, understand how foundation models and agents work, and see where services such as Amazon Bedrock AgentCore and Amazon SageMaker AI fit into a modern AI stack. From there, the book guides you through preparing data, building an MVP, deploying production-ready applications, and designing for scalability, governance, and responsible AI.Real-world industry examples and practical decision frameworks help you evaluate when to use generative AI, when traditional approaches are a better fit, and how to measure business value. You also examine advanced topics such as agentic AI, emerging patterns, and the future direction of enterprise AI.By the end of this book, you will be able to plan, build, and govern generative AI solutions on AWS that deliver measurable value for your organization.What you will learnUnderstand how generative AI creates business valueCompare models, prompts, fine-tuning, and RAGNavigate the AWS generative AI stack with confidencePrepare data and select models for real use casesBuild MVPs and production-ready AI applicationsApply governance, ethics, and responsible AI controlsEvaluate agentic AI patterns and emerging trendsMeasure impact across enterprise AI initiativesWho this book is forDevelopers, solutions architects, technical product managers, innovation leaders, CTOs, and business decision-makers who want to plan, build, and scale generative AI solutions on AWS. It is ideal for teams moving from experimentation to production and for leaders aligning AI initiatives with business outcomes. A basic understanding of cloud concepts and software delivery is helpful.
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Generative AI for Communications Systems
Comprehensive review of state-of-the-art research and development in Generative AI for future communications and networking Generative AI for Communications Systems provides a systematic foundation of knowledge on Generative AI for communications and networking. This book discusses the great potential and challenges in applying Generative AI as promising solutions to future communications systems and enables and facilitates “Generative AI as a Service” by exploring novel communications, networking architectures, protocols, and research trends. The book also includes information on: Crucial challenges to solve in Generative AI, such as training data availability, computational complexity, generalization for various scenarios, robustness of noisy and incomplete data, and real-time adaptation in communications and networking systemsCybersecurity concerns such as ethics and privacy in relation to Generative AIApplications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layerCommunications and networking solutions to meet the computing and communications challenges and demands to train and deploy large-scale Generative AI models Generative AI for Communications Systems is an excellent up-to-date resource on the subject for scholars and researchers in the fields of communications, artificial intelligence, machine learning, and network optimization as well as professionals working in the communications industry including engineers, network architects, and system designers.
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Generative AI and Large Language Models: Opportunities, Challenges, and Applications
This book provides a comprehensive exploration of the transformative impact of AI technologies across diverse fields. From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs). With a focus on both opportunities and challenges, the book examines the architectural challenges of transformer-based models, the ethical implications of AI, and the importance of language-specific adaptations, particularly for low-resource languages like Arabic. It also highlights the role of AI in code development, multimodal applications, and its integration with intellectual property frameworks. This book is an essential resource for researchers, practitioners, and policymakers seeking to understand and harness the potential of AI to drive innovation and global progress.
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Practical Guide to Machine Learning, NLP, and Generative AI: Libraries, Algorithms, and Applications
This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment.The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications.Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks.Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques.Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models.The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series
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Generative AI for Sign Language Recognition and Translation
Sign languages differ fundamentally from spoken and written languages, with their own grammar, syntax, and three-dimensional expression involving hand gestures, facial expressions, body movements, and spatial relationships. These non-manual elements are crucial in conveying grammatical structures, nuances, and emotional tones, making sign languages uniquely complex communication systems. This book provides a comprehensive foundation for understanding the linguistic structures of sign languages and explores the application of artificial intelligence (AI) techniques - ranging from classical machine learning to deep learning and generative AI - for developing effective sign language translation systems. It offers an end-to-end overview, covering linguistic fundamentals, available datasets, text-to-sign and speech-to-sign translation, vision-based sign recognition, pose estimation, and video-based sign language generation. Dedicated chapters focus on model architectures, dataset curation strategies, evaluation metrics, benchmarking tools, and human-centered design approaches for accessible communication systems. Ethical considerations and responsible AI practices are also discussed to promote the development of inclusive and equitable sign language technologies. Complemented by Python code examples, downloadable resources, and implementation insights, this book serves as a practical guide for researchers, engineers, students, and technology professionals aiming to develop AI-powered sign language systems. The multidisciplinary content also supports linguists, accessibility advocates, and application developers working on inclusive language technologies. With its broad coverage and practical orientation, this book is suited to academic and industry professionals in artificial intelligence, computer vision, natural language processing, human-computer interaction, speech technology, and accessibility research, as well as students and early-career researchers seeking a well-rounded introduction to AI-driven sign language translation. By bridging AI methodologies with real-world sign language applications, this book promotes the development of inclusive AI systems supporting communication accessibility for diverse populations.
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