Hands-on large language models : (Record no. 15096)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03184cam a22003377i 4500 |
| 001 - CONTROL NUMBER | |
| control field | TB12526 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | IN-BhIIT |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260508172028.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250503t20242024caua b 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789355425522 (pbk.) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | IN-BhIIT |
| 041 ## - LANGUAGE CODE | |
| Language code of text | eng |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.35 |
| Book number | ALA/H |
| 100 1# - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Alammar, Jay, |
| Relator term | Author. |
| 245 10 - TITLE STATEMENT | |
| Title | Hands-on large language models : |
| Sub Title | language understanding and generation / |
| Statement of responsibility, etc | Jay Alammar and Maarten Grootendorst. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Navi Mumbai : |
| Name of publisher | Shroff Publishers and Distributors Pvt. Ltd.; |
| Year of publication | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xix, 403 pages : |
| Other physical details(ill.) | illustrations (some color) ; |
| Dimensions(size) | 24 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Natural language generation (Computer science) |
| Topical Term | Artificial intelligence |
| General subdivision | Computer programs. |
| Topical Term | Machine learning. |
| Topical Term | Software engineering. |
| Topical Term | Artificial intelligence |
| General subdivision | Engineering applications. |
| Topical Term | Generative programming (Computer science) |
| Topical Term | Application software |
| General subdivision | Development. |
| Topical Term | Intelligence artificielle |
| General subdivision | Logiciels. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Grootendorst, Maarten, |
| Relator term | Joint author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Text Book |
| Koha issues (borrowed), all copies | 9 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Not withdrawn | Not Lost | not damaged | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 30/07/2025 | 22 | 1706.25 | 006.35 ALA/H | TB12524 | 2275.00 | 30/07/2025 | Text Book | ||
| Not withdrawn | Not Lost | not damaged | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 30/07/2025 | 22 | 1706.25 | 006.35 ALA/H | TB12523 | 2275.00 | 30/07/2025 | Text Book | ||
| Not withdrawn | Not Lost | not damaged | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 30/07/2025 | 22 | 1706.25 | 006.35 ALA/H | TB12525 | 2275.00 | 30/07/2025 | Text Book | ||
| Not withdrawn | Not Lost | not damaged | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 30/07/2025 | 22 | 1706.25 | 006.35 ALA/H | TB12526 | 2275.00 | 30/07/2025 | Text Book | ||
| Not withdrawn | Not Lost | not damaged | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 30/07/2025 | 22 | 1706.25 | 006.35 ALA/H | TB12522 | 2275.00 | 30/07/2025 | Course Reserve |