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Hands-On Large Language Models: - Paperback, by Alammar Jay; Grootendorst - Good

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Last updated on 15 Jul, 2025 01:05:55 BSTView all revisionsView all revisions

Item specifics

Condition
Good: A book that has been read, but is in good condition. Minimal damage to the book cover eg. ...
Book Title
Hands-On Large Language Models: Language Understanding and Genera
ISBN
9781098150969
Category

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1098150961
ISBN-13
9781098150969
eBay Product ID (ePID)
21070937210

Product Key Features

Number of Pages
425 Pages
Publication Name
Hands-On Large Language Models : Language Understanding and Generation
Language
English
Publication Year
2024
Subject
Data Modeling & Design, Intelligence (Ai) & Semantics, Computer Science, Natural Language Processing
Type
Textbook
Subject Area
Computers
Author
Jay Alammar, Maarten Grootendorst
Format
Trade Paperback

Dimensions

Item Height
0.9 in
Item Weight
25.9 Oz
Item Length
9 in
Item Width
7.1 in

Additional Product Features

LCCN
2025-421692
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/5
Synopsis
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 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 Number
QA76.9.N38A43 2024

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