000			03184cam a22003377i 4500
001			TB12526
003			IN-BhIIT
005			20260508172028.0
008			250503t20242024caua b 001 0 eng d
020			_a9789355425522 (pbk.)
040			_aIN-BhIIT
041			_aeng
082	0	4	_a006.35 _bALA/H
100	1		_aAlammar, Jay, _eAuthor. _926370
245	1	0	_aHands-on large language models : _blanguage understanding and generation / _cJay Alammar and Maarten Grootendorst.
260			_aNavi Mumbai : _bShroff Publishers and Distributors Pvt. Ltd.; _c2024.
300			_axix, 403 pages : _billustrations (some color) ; _c24 cm
504			_aIncludes bibliographical references and index.
505	0		_aPart 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.
520			_aAI 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.
650		0	_aNatural language generation (Computer science) _926371
650		0	_aArtificial intelligence _xComputer programs. _922110
650		0	_aMachine learning. _926982
650		0	_aSoftware engineering. _9982
650		0	_aArtificial intelligence _xEngineering applications. _926372
650		0	_aGenerative programming (Computer science) _925521
650		0	_aApplication software _xDevelopment. _91386
650		6	_aIntelligence artificielle _xLogiciels. _926373
700	1		_aGrootendorst, Maarten, _eJoint author. _926374
942			_cTB _09
999			_c15096 _d15096