Applied GPT-4 Systems: Definitive Reference for Developers and Engineers

"Applied GPT-4 Systems"

"Applied GPT-4 Systems" offers a comprehensive, expert-level exploration of building, deploying, and optimizing state-of-the-art generative AI using the transformative capabilities of GPT-4. It begins with a detailed breakdown of the model’s architecture, tracing the evolution of transformer models to the innovations and emergent behaviors unique to large language models at scale. Insights into parameter efficiency, advanced training techniques, and management of extensive context windows underscore the technical rigor and depth of coverage, ensuring readers develop a robust understanding of the foundation of GPT-4 systems.

The book systematically advances through the essential elements of training and customizing GPT-4, delving into massive data acquisition, efficient distributed training, and the complex pipelines that enable real-world, mission-critical deployments. Readers will learn advanced strategies for model finetuning, domain specialization, and continual learning, while also mastering the intricacies of scalable infrastructure, performance optimization, and cost management required for enterprise-grade AI solutions. Each chapter is buttressed by real-world best practices for security, privacy, compliance, and robust model evaluation, equipping practitioners to build systems that are not only powerful, but also trustworthy and reliable.

Finally, "Applied GPT-4 Systems" explores the cutting edge of AI application, from orchestrating conversational agents and multi-modal systems to embedding generative models in highly personalized, context-aware workflows and autonomous digital agents. With a forward-looking perspective on scaling, federated learning, explainable AI, and emerging research frontiers, this volume serves as both a technical reference and a strategic roadmap for professionals, researchers, and architects shaping the next generation of applied artificial intelligence with GPT-4.

© 2025 HiTeX Press (eBook): 6610000811403

Fecha de lanzamiento

eBook: 28 de mayo de 2025