{"product_id":"graph-neural-networks-for-real-world-ai-systems-architectures-training-pipelines-and-scalable-graph-intelligence-with-gcn-gat-graphsage-and-pyto-9798277250228","title":"Graph Neural Networks for Real-World AI Systems: Architectures, Training Pipelines, and Scalable Graph Intelligence with GCN, GAT, GraphSAGE, and PyTo","description":"\u003cp\u003e • Author(s): Tyrell Owen\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Computer Science\u003c\/p\u003e\u003cp\u003eUnlock the transformative power of \u003cb\u003eGraph Neural Networks (GNNs) \u003c\/b\u003eand elevate your AI systems with contextual intelligence and relational reasoning. This comprehensive guide bridges the gap between cutting-edge research and practical application, empowering data scientists, AI engineers, and machine learning practitioners to design, implement, and scale graph-based AI systems for real-world challenges.\u003cbr\u003eIn today's complex data environments, relationships between entities are just as crucial as the entities themselves. Traditional deep learning approaches often struggle to capture these intricate connections, limiting performance in domains like recommendation systems, fraud detection, knowledge graphs, and social network analysis. This book dives deep into Graph Neural Networks-offering a hands-on roadmap to leverage GCN, GAT, GraphSAGE, and PyTorch Geometric for building high-performance, explainable, and scalable AI systems.\u003cbr\u003e\u003cb\u003eWhat you'll gain from this book: \u003c\/b\u003e\u003c\/p\u003e\u003col\u003e\n\u003cli\u003e\n\u003cb\u003eFoundations of Graph Intelligence: \u003c\/b\u003e Develop a solid understanding of graph theory, graph data structures, and relational representations as the backbone of modern AI systems.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eArchitectures for Real-World Applications: \u003c\/b\u003e Explore Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, with practical insights on selecting and customizing architectures for specific use cases.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eScalable Training Pipelines: \u003c\/b\u003e Learn to design efficient data pipelines, mini-batch training strategies, and distributed computing approaches for large-scale graphs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eIntegration with PyTorch Geometric: \u003c\/b\u003e Master hands-on implementation, from preprocessing graph data to deploying GNN models using the widely adopted PyTorch Geometric framework.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eCase Studies \u0026amp; Practical Examples: \u003c\/b\u003e Dive into real-world projects demonstrating social network analytics, knowledge graph completion, recommendation engines, and fraud detection using GNNs.\u003c\/li\u003e\n\u003c\/ol\u003eWhether you are building AI systems for enterprise-scale applications or exploring the forefront of research in graph intelligence, this book equips you with the practical skills, architectural know-how, and strategic insights to harness the full potential of Graph Neural Networks. Ground your AI in relational reasoning, deliver explainable insights, and transform complex data into actionable intelligence.","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":46861949042839,"sku":"9798277250228","price":1289.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798277250228.webp?v=1769964149","url":"https:\/\/atlanticbooks.com\/products\/graph-neural-networks-for-real-world-ai-systems-architectures-training-pipelines-and-scalable-graph-intelligence-with-gcn-gat-graphsage-and-pyto-9798277250228","provider":"Atlantic Books","version":"1.0","type":"link"}