Auf Lager:
1
Buch (Softcover): Fachbuch
Hands-On Large Language Models
Language Understanding and Generation
Produkt bewerten
Verlag:
O'Reilly Unsere-Artikel-Nr.: 3R6QWY5
EAN: 9781098150969
Auf Lager:
1
Zustellung: Di, 02.06.2026
Versand: Kostenlos
-19.5 %
CHF 105.–
CHF 84.56
Beschreibung
AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. Through the visually educational nature of this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning Jay Alammar is Director and Engineering Fellow at Cohere (pioneering provider of large language models as an API). Maarten Grootendorst is a Senior Clinical Data Scientist at Netherlands Comprehensive Cancer Organization (IKNL).
Spezifikationen
Sprache
- Englisch
Autor
- Jay Alammar
- Maarten Grootendorst
Erscheinungsjahr
- 2024
Format
- Buch (Softcover)