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Hands-On Large Language Models : Language Understanding and Generation by Jay Alammar and Maarten Grootendorst (2024, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated
ISBN-101098150961
ISBN-139781098150969
eBay Product ID (ePID)21070937210

Product Key Features

Number of Pages425 Pages
Publication NameHands-On Large Language Models : Language Understanding and Generation
LanguageEnglish
Publication Year2024
SubjectData Modeling & Design, Intelligence (Ai) & Semantics, Computer Science, Natural Language Processing
TypeTextbook
AuthorJay Alammar, Maarten Grootendorst
Subject AreaComputers
FormatTrade Paperback

Dimensions

Item Height0.9 in
Item Weight25.9 Oz
Item Length9 in
Item Width7.1 in

Additional Product Features

LCCN2025-421692
Dewey Edition23
IllustratedYes
Dewey Decimal006.3/5
SynopsisAI 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 representation Build advanced LLM pipelines to cluster text documents and explore the topics they cover Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation Gain 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
LC Classification NumberQA76.9.N38A43 2024

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