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001			978-3-031-65647-7
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100	1		_aKamath, Uday. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aLarge Language Models: A Deep Dive _h[electronic resource] : _bBridging Theory and Practice / _cby Uday Kamath, Kevin Keenan, Garrett Somers, Sarah Sorenson.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024.
300			_aXXXIV, 472 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. Large Language Models: An Introduction -- 2. Pre-trained Models -- 3. Prompt-based Learning -- 4. LLM Adaptation and Utilization -- 5. Tuning for LLM Alignment -- 6. LLM Challenges and Solutions -- 7. Retrieval-Augmented Generation -- 8. LLMs in Production -- 9. Multimodal LLMs -- 10. LLMs: Evolution and New Frontiers -- Appendix.
520			_aLarge Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs-their intricate architecture, underlying algorithms, and ethical considerations-require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.
541			_fUABC ; _cPerpetuidad
650		0	_aNatural language processing (Computer science).
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
650	1	4	_aNatural Language Processing (NLP).
650	2	4	_aMachine Learning.
650	2	4	_aArtificial Intelligence.
700	1		_aKeenan, Kevin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSomers, Garrett. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSorenson, Sarah. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031656460
776	0	8	_iPrinted edition: _z9783031656484
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-65647-7
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
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