Editore: Amazon Digital Services LLC - Kdp
ISBN 13: 9798307943434
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 22,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware.
Editore: Amazon Digital Services LLC - Kdp, 2025
ISBN 13: 9798307943434
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 21,13
Quantità: Più di 20 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Editore: Amazon Digital Services LLC - Kdp, 2025
ISBN 13: 9798307943434
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 20,40
Quantità: Più di 20 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Editore: Independently Published, 2025
ISBN 13: 9798307943434
Da: CitiRetail, Stevenage, Regno Unito
EUR 24,04
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. Harness the potential of Retrieval Augmented Generation (RAG) to build more robust and reliable AI applications. This book provides a comprehensive, hands-on approach to implementing RAG using Python, focusing on real-world NLP and AI use cases. You'll explore: The core concepts of RAG and its advantages over traditional language models.Practical Python implementations using popular libraries for NLP, vector databases, and large language model APIs.Techniques for efficient information retrieval, including semantic search and vector embeddings.Strategies for optimizing RAG pipelines for performance and accuracy.Applications in question answering, chatbots, document summarization, and more.Whether you're a seasoned developer or just starting with AI, this book equips you with the knowledge and skills to build powerful, context-aware applications with RAG. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.