TY  - BOOK
AU  - Alammar,Jay
AU  - Grootendorst,Maarten
TI  - Hands-on large language models: language understanding and generation 
SN  - 9789355425522 (pbk.)
U1  - 006.35 
PY  - 2024///
CY  - Navi Mumbai
PB  - Shroff Publishers and Distributors Pvt. Ltd.
KW  - Natural language generation (Computer science)
KW  - Artificial intelligence
KW  - Computer programs
KW  - Machine learning
KW  - Software engineering
KW  - Engineering applications
KW  - Generative programming (Computer science)
KW  - Application software
KW  - Development
KW  - Intelligence artificielle
KW  - Logiciels
N1  - Includes bibliographical references and index; Part 1. Understanding language models.  An introduction to Large Language Models -- Tokens and embeddings -- Looking inside Large Language Models -- Part 2. Using pretrained language models.  Text classification -- Text clustering and topic modeling -- Prompt engineering -- Advanced text generation techniques and tools -- Semantic search and retrieval-augmented generation -- Mulitimodal Large Language Models -- Part 3. Training and fine-tuning language models.  Creating text embedding models -- Fine-tuning representation models for classification -- Fine-tuning generation models
N2  - 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 his 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 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
ER  -