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_a10.1007/979-8-8688-0569-1 _2doi |
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| 050 | 4 | _aTA347.A78 | |
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| 100 | 1 |
_aSingh, Bhawna. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _988322 |
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| 245 | 1 | 0 |
_aBuilding Applications with Large Language Models _h[electronic resource] : _bTechniques, Implementation, and Applications / _cby Bhawna Singh. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aBerkeley, CA : _bApress : _bImprint: Apress, _c2024. |
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| 300 |
_aXVII, 280 p. 149 illus., 126 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1: Introduction to Large Language Models -- Chapter 2: Understanding Foundation Models -- Chapter 3: Adapt with Fine-tuning -- Chapter 4: The Magic of Prompt Engineering -- Chapter 5: Stop Hallucination with RAG -- Chapter 6: Evaluation of LLM -- Chapter 7: Tools and Frameworks for Development -- Chapter 8: Run in Production.-Chapter 9: The Ethical Dilemma -- Chapter 10: Future of AI. | |
| 520 | _aThis book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will Learn Be able to answer the question: What are Large Language Models? Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases Know the best practices for effective implementation Know the metrics and frameworks essential for evaluating the performance of Large Language Models . | ||
| 650 | 0 |
_aArtificial intelligence. _958441 |
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| 650 | 0 |
_aMachine learning. _955059 |
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| 650 | 0 |
_aPython (Computer program language). _964608 |
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| 650 | 0 |
_aNatural language processing (Computer science). _965131 |
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| 650 | 1 | 4 |
_aArtificial Intelligence. _958441 |
| 650 | 2 | 4 |
_aMachine Learning. _955059 |
| 650 | 2 | 4 |
_aPython. _947553 |
| 650 | 2 | 4 |
_aNatural Language Processing (NLP). _966164 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868805684 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868805707 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/979-8-8688-0569-1 |
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| 912 | _aZDB-2-SXPC | ||
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