Search results for "Generative AI System Design"
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R 1,368.00
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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System Design
System Design: A Practical Guide with SpecC presents the system design flow following a simple example through the whole process in an easy-to-follow, step-by-step fashion. Each step is described in detail in pictorial form and with code examples in SpecC. For each picture slide a detailed explanation is provided of the concepts presented. This format is suited for tutorials, seminars, self-study, as a guided reference carried by examples, or as teaching material for courses on system design. Features: Comprehensive introduction to and description of the SpecC language and design methodology; IP-centric language and methodology with focus on design reuse; Complete framework for system-level design from specification to implementation for SOCs and other embedded HW/SW systems. System Design: A Practical Guide with SpecC will benefit designers and design managers of complex SOCs, or embedded systems in general, by allowing them to develop new methodologies from these results, in order to increase design productivity by orders of magnitude. Designers at RTL, logical or physical levels, who are interested in moving up to the system level, will find a comprehensive overview within. The design models in the book define IP models and functions for IP exchange between IP providers and their users. A well-defined methodology like the one presented in this book will help product planning divisions to quickly develop new products or to derive completely new business models, like e-design or product-on-demand. Finally, researchers and students in the area of system design will find an example of a formal, well-structured design flow in this book.
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Visualizing Generative AI
Generative AI has the potential to innovate and evolve business processes, but workers are still figuring out how to build with, optimize, and prompt GenAI tools to fit their needs. And of course, there are pitfalls to avoid, like security risks and hallucinations. Getting it right requires an intuitive understanding of the technology's capabilities and limitations. This approachable guidebook helps learners of all levels navigate GenAI - and have fun while doing it.Loaded with insightful diagrams and illustrations, Visualizing Generative AI is the perfect entry point for curious IT professionals, business leaders who want to stay on top of the latest technologies, students exploring careers in cloud computing and AI, and anyone else interested in getting started with GenAI. You'll traverse the generative AI landscape, exploring everything from how this technology works to the ways organizations are already leveraging it to great success.Understand how generative AI has evolved, with a focus on major breakthroughsGet acquainted with the available tools and platforms for GenAI workloadsExamine real-world applications, such as chatbots and workflow automationLearn fundamentals that you can build upon as you continue your GenAI journey
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Generative AI Foundations in Python
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application.Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs.By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learnDiscover the fundamentals of GenAI and its foundations in NLPDissect foundational generative architectures including GANs, transformers, and diffusion modelsFind out how to fine-tune LLMs for specific NLP tasksUnderstand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as financeExplore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAGImplement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputsWho this book is forThis book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
R 1,368.00
Designing Machine Learning Systems
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.This book will help you tackle scenarios such as:Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems
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Essential Guide to AI Companions
Build responsible AI companions with practical ethical frameworks for developers The Essential Guide to AI Companions: From Technical Foundations to Social Impact provides developers and researchers with structured frameworks for building AI companions that account for both technical design and the psychology of human-AI relationships. Written by the former Principal Ethicist at Hugging Face, this guide examines how architecture decisions shape attachment dynamics, user trust, and social impact across the entire development lifecycle. From system architecture to deployment monitoring, this guide helps teams anticipate challenges around attachment dynamics, context manipulation, and user wellbeing before they emerge in production. Drawing from real-world cases across the AI companion landscape, it provides actionable strategies for content moderation policies, ethical framework implementation, and ongoing system maintenance that safeguard users while enabling meaningful interactions. You'll also discover: Practical frameworks for weaving ethical considerations naturally into technical workflows throughout the development processStrategies for addressing trust, safety, and social impact challenges that typically surface only after deploymentMethods for building content moderation systems that balance user freedom with wellbeing protectionsTechniques for evaluating how users form emotional bonds with AI systems, drawing on attachment theory and parasocial relationship research, and what this means for architecture decisionsApproaches to monitoring and maintaining AI companions that contribute positively to human-AI interaction Ideal for developers building chatbots and AI companions, researchers exploring human-AI relationships, and executives overseeing AI product development, this guide delivers the structured frameworks needed to create AI companions that enhance user experience while maintaining ethical standards throughout the development lifecycle.
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Generative AI in Cybersecurity
This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation. The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception. This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting.
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Charting the Intelligence Frontiers – Edge AI Systems Nexus
We are witnessing a fundamental shift in the computing landscape, a paradigm shift where intelligence is rapidly migrating to the very edges of the network. This is the era of edge AI, which embeds decision-making, perception, and control directly into devices that shape our physical world. This book is a selected collection of the presented work from the European Conference on EDGE AI Technologies and Applications (EEAI) held on 21–23 October 2024 in Cagliari, Sardinia, Italy. The conference is part of a series of annual conferences that delve into the expanding continuum of micro-, deep-, and meta-edge architectures, which form the backbone of emerging intelligent autonomous systems across numerous sectors. From the complexities of advanced algorithms and the design of novel edge AI hardware accelerators to the architecture of next-generation communication networks, AI frameworks and software systems, the EEAI fosters a vibrant exchange of ideas that are following the future and actively defining it. From industrial automation to environmental monitoring, from manufacturing to smart mobility, the expansion of micro-, deep-, and meta-edge AI processing is propelling a new class of autonomous systems. These systems thrive on distributed intelligence, leveraging advancements in edge AI hardware architectures, accelerators, performant algorithms, and advanced software frameworks. The chapters in this volume celebrate this paradigm shift and offer a glimpse into the vibrant ecosystem shaping the future of edge AI technologies and applications. The numerous chapters describe novel solutions and rigorous investigations spanning verification and validation, federated learning, neuromorphic design, scalable architectures, sensor fusion, and human–machine collaboration, each chapter demonstrating the innovation wave fuelling Europe's edge AI landscape. Together, these twenty chapters provide a rich, multi-layered, and deeply insightful perspective on the state of edge AI. Each chapter documents the achievements made and highlights the path forward, offering a compelling vision of a future where intelligence is seamlessly and securely integrated into the fabric of the real world. This book is an essential guide for navigating, understanding, and contributing to the dynamic and rapidly evolving field of edge AI. The real value of this book lies in its innovative, forward-looking perspective, offering a guided exploration of the latest scientific breakthroughs and practical advancements shaping intelligent systems at the edge. For researchers, students, practitioners, and visionaries, this book provides a comprehensive roadmap for the next stage in the evolution of intelligent, connected systems at the edge.
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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.
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Next-Generation Computing and Information Systems
This book compiles the proceedings of the Third International Conference on Next-Generation Computing and Information Systems (ICNGCIS 25). It combines high-quality research, practical insights, and scholarly debate spanning traditional domains like distributed computing, networks, and cybersecurity, alongside emerging areas such as AI, IoT, quantum security, and edge computing.The proceedings include papers addressing currently relevant research issues such as smart contract security, interoperability in the metaverse, AI applications in healthcare, agriculture and related domains. The proceedings present findings with real-world implications for modern computing and information systems, addressing key challenges in design, deployment, operations, performance optimization, and limitation mitigation.This book targets researchers from academia and industry, practitioners, students, technology enthusiasts, and general audiences seeking to understand cutting-edge applications, practical use cases, and core principles of modern computing and information systems.
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Google Machine Learning and Generative AI for Solutions Architects
Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectivelyKey FeaturesUnderstand key concepts, from fundamentals through to complex topics, via a methodical approachBuild real-world end-to-end MLOps solutions and generative AI applications on Google CloudGet your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecyclePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer.You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learnBuild solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and SparkSource, understand, and prepare data for ML workloadsBuild, train, and deploy ML models on Google CloudCreate an effective MLOps strategy and implement MLOps workloads on Google CloudDiscover common challenges in typical AI/ML projects and get solutions from expertsExplore vector databases and their importance in Generative AI applicationsUncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflowsWho this book is forThis book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.
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Gen AI-assisted Agile Course, Project-Based Teaching and Learning
This title represents an essential resource for understanding the evolving landscape of AI-augmented pedagogy, examining the transformative effects of generative artificial intelligence (GenAI) on education through the lens of agile course design and project-based learning.Through a detailed analysis of diverse empirical case studies from eight countries between 2015 and 2024, the authors effectively illustrate the dual impact of GenAI on education, academic integrity, and teacher autonomy. They demonstrate how GenAI can enhance personalized STEM education and STREAM (Science, Technology, Reading and Writing, Engineering, Arts and Mathematics) instruction, as well as project-based learning, while simultaneously presenting significant challenges. They provide a nuanced assessment of the opportunities and disruptions introduced by GenAI and offer concrete pedagogical strategies for teaching practitioners.This book serves as a vital reference for scholars in GenAI, educational technology and curriculum, and instruction. It also provides practical, ready-to-implement pedagogical approaches for pre-service teachers in higher education and primary and secondary schools.
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Generative AI Techniques for Sustainability in Healthcare Security
In a world of constant change, sustainability and technology emerge as pivotal elements in healthcare. Generative artificial intelligence (AI) presents the capabilities of more accurate diagnoses, personalized treatment plans, and drug discovery, while certain operations in healthcare, such as managing relationships with healthcare systems often necessitate a human touch, these processes can be augmented by generative AI. Sustainability and health security are becoming increasingly important. The relationship between sustainability and health security is significant, as environmental factors such as air pollution, climate change, and access to green spaces can all affect human health. Generative AI Techniques for Sustainability in Healthcare Security provides a comprehensive understanding of generative AI techniques and their application for sustainability in health security, empowering readers with the knowledge needed to leverage these cutting-edge technologies effectively. Covering topics such as disease detection, drug discovery and development, and sustainability, this book is a valuable resource for scientists, medical professionals, hospital administrators, researchers, technologists, academicians, and more.
R 8,919.50
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