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Hands-On Large Language Models: Language Understanding and Generation

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Marque : GENERIC
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AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practi...

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Description produit

Marque
GENERIC
Titre
Hands-On Large Language Models: Language Understanding and Generation
Éditeur
O'Reilly Media
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
10/15/2024 12:00:00 AM
Langue d'origine
English
ISBN
1098150961
Dimensions
7 x 0.87 x 9.19 inches
Nombre de pages
425 pages
Langue
English
Résumé
AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today. You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings. This book also helps you: Understand the architecture of Transformer language models that excel at text generation and representationBuild advanced LLM pipelines to cluster text documents and explore the topics they coverBuild semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankersExplore how generative models can be used, from prompt engineering all the way to retrieval-augmented generationGain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning Read more
Auteur
Jay Alammar, Maarten Grootendorst
Date de parution
10/15/2024 12:00:00 AM