03184cam a22003377i 4500 TB12526 IN-BhIIT 20260508172028.0 250503t20242024caua b 001 0 eng d 9789355425522 (pbk.) IN-BhIIT eng 006.35 ALA/H Alammar, Jay, Author. 26370 Hands-on large language models : language understanding and generation / Jay Alammar and Maarten Grootendorst. Navi Mumbai : Shroff Publishers and Distributors Pvt. Ltd.; 2024. xix, 403 pages : illustrations (some color) ; 24 cm Includes bibliographical references and index. Part 1. Understanding language models. An introduction to Large Language Models -- Tokens and embeddings -- Looking inside Large Language Models -- Part 2. Using pretrained language models. Text classification -- Text clustering and topic modeling -- Prompt engineering -- Advanced text generation techniques and tools -- Semantic search and retrieval-augmented generation -- Mulitimodal Large Language Models -- Part 3. Training and fine-tuning language models. Creating text embedding models -- Fine-tuning representation models for classification -- Fine-tuning generation models. 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 his 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 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. Natural language generation (Computer science) 26371 Artificial intelligence Computer programs. 22110 Machine learning. 26982 Software engineering. 982 Artificial intelligence Engineering applications. 26372 Generative programming (Computer science) 25521 Application software Development. 1386 Intelligence artificielle Logiciels. 26373 Grootendorst, Maarten, Joint author. 26374 TB 9 15096 15096 0 0 ddc 0 0 SES CLIITBBS CLIITBBS 2025-07-30 22 1706.25 3 006.35 ALA/H TB12524 2026-02-26 14:07:54 2026-02-05 2275.00 2025-07-30 TB 0 0 ddc 0 0 SES CLIITBBS CLIITBBS 2025-07-30 22 1706.25 1 2 006.35 ALA/H TB12523 2026-03-24 2025-09-18 14:30:57 2025-09-18 2275.00 2025-07-30 TB 0 0 ddc 0 0 SES CLIITBBS CLIITBBS 2025-07-30 22 1706.25 3 006.35 ALA/H TB12525 2026-06-08 2026-05-08 17:20:28 2026-05-08 2275.00 2025-07-30 TB 0 0 ddc 0 0 SES CLIITBBS CLIITBBS 2025-07-30 22 1706.25 2 2 006.35 ALA/H TB12526 2026-05-12 15:35:15 2025-12-21 2275.00 2025-07-30 TB 0 0 ddc 0 0 SES CLIITBBS CLIITBBS 2025-07-30 22 1706.25 0 006.35 ALA/H TB12522 2025-07-30 00:00:00 2275.00 2025-07-30 RES