Hands-On Large Language Models: Language Understanding and Generation
Author: Jay Alammar (Author), Maarten Grootendorst (Author)
Publisher finelybook 出版社: O’Reilly Media
Edition 版次: 1st
Publication Date 出版日期: 2024-10-15
Language 语言: English
Print Length 页数: 425 pages
ISBN-10: 1098150961
ISBN-13: 9781098150969
Book Description
AI 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 this 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 large 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
Review
– Andrew Ng, Founder of DeepLearning AI “I can’t think of another book that is more important to read right now. On every single page, I learned something that is critical to success in this era of language models.”
-Josh Starmer, StatQuest “This is an exceptional guide to the world of language models and their practical applications in industry. Its highly-visual coverage of generative, representational, and retrieval applications of language models empowers readers to quickly understand, use, and refine LLMs. Highly recommended!”
-Nils Reimers, Director of Machine Learning at Cohere | creator sentence-transformers “If you’re looking to get up to speed in everything regarding LLMs, look no further! In this wonderful book, Jay and Maarten will take you from zero to expert in the history and latest advances in large language models. With intuitive explanations, great real-life examples, clear illustrations, and comprehensive code labs, this book lifts the curtain on the complexities of transformer models, tokenizers, semantic search, RAG, and many other cutting-edge technologies. A must read for anyone interested in the latest AI technology!”
– Luis Serrano, PhD, Founder and CEO – Serrano Academy “This book is a must-read for anyone interested in the rapidly-evolving field of generative AI. With a focus on both text and visual embeddings, it’s a great blend of algorithmic evolution, theoretical rigor, and practical guidance. Whether you are a student, researcher, or industry professional, this book will equip you with the use cases and solutions needed to level-up your knowledge of generative AI. Well done!”
– Chris Fregly, Principal Solution Architect, Generative AI at AWS
About the Author
Maarten Grootendorst is a Senior Clinical Data Scientist at IKNL (Netherlands Comprehensive Cancer Organization). He holds master’s degrees in organizational psychology, clinical psychology, and data science which he leverages to communicate complex Machine Learning concepts to a wide audience. With his popular blogs, he has reached millions of readers by explaining the fundamentals of Artificial Intelligence–often from a psychological point of view. He is the author and maintainer of several open-source packages that rely on the strength of Large Language Models, such as BERTopic, PolyFuzz, and KeyBERT. His packages are downloaded millions of times and used by data professionals and organizations worldwide.
