Search results for "Generative AI Math"
Items we need to order for you
R 764.00
R 1,008.00
R 305.00
R 977.00
R 2,442.00
Random Generation of Trees
Random Generation of Trees is about a field on the crossroads between computer science, combinatorics and probability theory. Computer scientists need random generators for performance analysis, simulation, image synthesis, etc. In this context random generation of trees is of particular interest. The algorithms presented here are efficient and easy to code. Some aspects of Horton--Strahler numbers, programs written in C and pictures are presented in the appendices. The complexity analysis is done rigorously both in the worst and average cases. Random Generation of Trees is intended for students in computer science and applied mathematics as well as researchers interested in random generation.
R 5,988.36
R 814.00
R 753.00
R 1,596.00
R 912.00
R 244.00
R 672.00
R 475.00
R 886.00
R 763.00
R 763.00
R 854.00
R 519.00
R 1,518.86
R 458.00
R 611.00
R 611.00
R 844.00
Trust in Generative Artificial Intelligence
Trust in Generative Artificial Intelligence: Human‑Robot Interaction and Ethical Considerations is a comprehensive exploration of the critical role of trust in the rapidly evolving world of AI. Edited by Joanna Paliszkiewicz, Ireneusz Dąbrowski, and Leila Halawi, this book provides a thorough examination of how trust influences the integration and effectiveness of generative AI in various aspects of society. The book spans three main sections, offering insights from international experts across multiple disciplines and a blend of theoretical foundations and practical insights, making it a valuable resource for both understanding and implementing trust in AI systems. Readers will benefit from in‑depth analyses of emerging trends, systematic literature reviews, and case studies on trust in diverse contexts, from social media to healthcare. The focus on human‑robot interaction and ethical considerations provides a nuanced understanding of the psychological and ethical factors at play. Aimed at researchers, practitioners, and policymakers interested in AI, ethics, and human‑robot interaction, this edited collection is also suitable for educators and students in fields related to AI and technology, providing a comprehensive resource for academic study and practical application.
R 2,381.00
Advanced Retrieval-Augmented Generation
Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation Large language models are powerful—but they hallucinate. Advanced Retrieval-Augmented Generation offers a complete guide from the foundations of information retrieval (IR) to the cutting-edge frontiers of RAG. Bridging large language models (LLMs) and knowledge graphs (KGs), this book provides the theoretical principles, practical techniques, and hands-on frameworks needed to build reliable AI systems that minimize hallucinations and improve factual correctness. The book covers core concepts of Graph-RAG with applications across search, recommendation, and enterprise AI. Practical chapters demonstrate implementations using LlamaIndex, Neo4j, and leading Graph-RAG frameworks. Readers will learn: IR and LLM fundamentals — model paradigms, transformer architecture, model families, training techniques, prompt engineering, applications, and limitationsRAG pipeline engineering — chunking, indexing, retrieval, ranking, and generationKG construction and analytics — schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation — graph-based retrieval, graph-assisted generation, hybrid LLM–KG workflows, frameworks, benchmarks, and metricsEmerging directions — multimodal KGs, dynamic graphs, explainable RAG, RL-based traversal, and enterprise-scale implementations With extensive hands-on examples and production-ready patterns, Advanced Retrieval-Augmented Generation is an indispensable resource for AI practitioners, ML engineers, researchers, and architects building the next generation of reliable, knowledge-grounded AI systems.
R 4,274.00
R 641.00
R 722.16
R 456.00
R 458.00
R 763.00
Building Trust in the Generative Artificial Intelligence Era
In an era where generative artificial intelligence (AI) is reshaping industries and daily life, trust has become a cornerstone for its successful adoption and application. Building Trust in the Generative Artificial Intelligence Era: Technology Challenges and Innovations explores how trust can be built, maintained, and evaluated in a world increasingly reliant on AI technologies. Designed to be accessible to a broad audience, thi book blends theoretical insights with practical approaches, offering readers a comprehensive understanding of the topic.This book is divided into three parts. The first part examines the foundations of trust in generative AI, highlighting trends and ethical challenges such as "greenwashing" and remote work dynamics. The second part provides actionable frameworks and tools for assessing and enhancing trust, focusing on topics like cybersecurity, transparency, and explainability. The final section presents global case studies exploring university students' perceptions of ChatGPT, generative AI's applications in European agriculture, and its transformative impact on financial systems.By addressing both the opportunities and risks of generative AI, this book delivers groundbreaking insights for academics, professionals, and policymakers worldwide. It emphasizes practical solutions, ensuring readers gain the knowledge needed to navigate the evolving technological landscape and foster trust in transformative AI systems.
R 2,381.00
