Drew Farris – författare
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3 produkter
3 produkter
Häftad, Engelska, 2025
429 kr
Skickas inom 7-10 vardagar
Learn how large language models like GPT and Gemini work under the hood in plain English.How Large Language Models Work translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free.In How Large Language Models Work you will learn how to:
Test and evaluate LLMs Use human feedback, supervised fine-tuning, and Retrieval augmented generation (RAG) Reducing the risk of bad outputs, high-stakes errors, and automation bias Human-computer interaction systems Combine LLMs with traditional MLHow Large Language Models Work is written by some of the best machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. In clear and simple terms, these experts lay out the foundational concepts of LLMs, the technology’s opportunities and limitations, and best practices for incorporating AI into your organization.
E-bok
Engelska, 2012395 kr
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SummaryTaming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are built.About this BookThere is so much text in our lives, we are practically drowningin it. Fortunately, there are innovative tools and techniquesfor managing unstructured information that can throw thesmart developer a much-needed lifeline. You''ll find them in thisbook.Taming Text is a practical, example-driven guide to working withtext in real applications. This book introduces you to useful techniques like full-text search, proper name recognition,clustering, tagging, information extraction, and summarization.You''ll explore real use cases as you systematically absorb thefoundations upon which they are built.Written in a clear and concise style, this book avoids jargon, explainingthe subject in terms you can understand without a backgroundin statistics or natural language processing. Examples arein Java, but the concepts can be applied in any language.Written for Java developers, the book requires no prior knowledge of GWT. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read.What''s InsideWhen to use text-taming techniquesImportant open-source libraries like Solr and MahoutHow to build text-processing applicationsAbout the AuthorsGrant Ingersoll is an engineer, speaker, and trainer, a Lucenecommitter, and a cofounder of the Mahout machine-learning project. Thomas Morton is the primary developer of OpenNLP and Maximum Entropy. Drew Farris is a technology consultant, software developer, and contributor to Mahout,Lucene, and Solr."Takes the mystery out of verycomplex processes."—From the Foreword by Liz Liddy, Dean, iSchool, Syracuse UniversityTable of ContentsGetting started taming textFoundations of taming textSearchingFuzzy string matchingIdentifying people, places, and thingsClustering textClassification, categorization, and taggingBuilding an example question answering systemUntamed text: exploring the next frontier
E-bok
Engelska, 2025394 kr
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Learn how large language models like GPT and Gemini work under the hood in plain English.How Large Language Models Work translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free. In How Large Language Models Work you will learn how to: • Test and evaluate LLMs • Use human feedback, supervised fine-tuning, and Retrieval Augmented Generation (RAG) • Reducing the risk of bad outputs, high-stakes errors, and automation bias • Human-computer interaction systems • Combine LLMs with traditional ML How Large Language Models Work is authored by top machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. They lay out how LLM and GPT technology works in plain language that’s accessible and engaging for all. About the Technology Large Language Models put the “I” in “AI.” By connecting words, concepts, and patterns from billions of documents, LLMs are able to generate the human-like responses we’ve come to expect from tools like ChatGPT, Claude, and Deep-Seek. In this informative and entertaining book, the world’s best machine learning researchers from Booz Allen Hamilton explore foundational concepts of LLMs, their opportunities and limitations, and the best practices for incorporating AI into your organizations and applications. About the Book How Large Language Models Work takes you inside an LLM, showing step-by-step how a natural language prompt becomes a clear, readable text completion. Written in plain language, you’ll learn how LLMs are created, why they make errors, and how you can design reliable AI solutions. Along the way, you’ll learn how LLMs “think,” how to design LLM-powered applications like agents and Q&A systems, and how to navigate the ethical, legal, and security issues. What’s Inside • Customize LLMs for specific applications • Reduce the risk of bad outputs and bias • Dispel myths about LLMs • Go beyond language processing About the Readers No knowledge of ML or AI systems is required. About the Author Edward Raff, Drew Farris and Stella Biderman are the Director of Emerging AI, Director of AI/ML Research, and machine learning researcher at Booz Allen Hamilton. Table of Contents 1 Big picture: What are LLMs? 2 Tokenizers: How large language models see the world 3 Transformers: How inputs become outputs 4 How LLMs learn 5 How do we constrain the behavior of LLMs? 6 Beyond natural language processing 7 Misconceptions, limits, and eminent abilities of LLMs 8 Designing solutions with large language models 9 Ethics of building and using LLMs