Training data for machine learning : (Record no. 141462)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01684nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260716141722.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260716s2024 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781492094524 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | SAR-T |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sarkis, Anthony |
| 245 10 - TITLE STATEMENT | |
| Title | Training data for machine learning : |
| Remainder of title | human supervision from annotation to data science / |
| Statement of responsibility, etc | Anthony Sarkis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Beijing : |
| Name of publisher, distributor, etc | O'Reilly, |
| Date of publication, distribution, etc | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxii, 306 p. |
| 500 ## - GENERAL NOTE | |
| General note | Index present |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts and other shareholders Successfully maintain, operate, and improve your system |
| 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 | Data mining |
| 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 | 2026-07-16 | 006.31 SAR-T | 153277 | 2026-07-16 | 2026-07-16 | Books |