Transformers for natural language processing and computer vision : (Record no. 141690)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02068nam a22002537a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260727164802.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260727s2024 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781805128724 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.35 |
| Item number | ROT-T |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rothman, Denis |
| 245 10 - TITLE STATEMENT | |
| Title | Transformers for natural language processing and computer vision : |
| Remainder of title | explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 / |
| Statement of responsibility, etc | Denis Rothman |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Birmingham : |
| Name of publisher, distributor, etc | Packt Publishing, |
| Date of publication, distribution, etc | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxxii, 691 p. |
| 490 ## - SERIES STATEMENT | |
| Series statement | Expert insight |
| 500 ## - GENERAL NOTE | |
| General note | Index present |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices"--Back cover |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-07-27 | 006.35 ROT-T | 153374 | 2026-07-27 | 2026-07-27 | Books |