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Building applications with large lan...

Singh, Bhawna.

Building applications with large language modelstechniques, implementation, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building applications with large language modelsby Bhawna Singh.
其他題名:	techniques, implementation, and applications /
作者:	Singh, Bhawna.
出版者:	Berkeley, CA :Apress :2024.
面頁冊數:	xvii, 280 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Natural language processing (Computer science)
電子資源:	https://doi.org/10.1007/979-8-8688-0569-1
ISBN:	9798868805691$q(electronic bk.)

Building applications with large language modelstechniques, implementation, and applications /
Singh, Bhawna.

Building applications with large language modelstechniques, implementation, and applications /[electronic resource] :by Bhawna Singh. - Berkeley, CA :Apress :2024. - xvii, 280 p. :ill. (some col.), digital ;24 cm.

Chapter 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.

This 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.

ISBN: 9798868805691$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0569-1doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

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520 $a This 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.
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