01684nam a22002177a 4500 20260716141722.0 260716s2024 |||||||| |||| 00| 0 eng d 9781492094524 PK-LaUMT 006.31 SAR-T Sarkis, Anthony 13716 Training data for machine learning : human supervision from annotation to data science / Anthony Sarkis Beijing : O'Reilly, 2024 xxii, 306 p. Index present 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 Eng Machine learning Data mining BK 141462 141462 0 0 0 0 01 01 2026-07-16 0 006.31 SAR-T 153277 2026-07-16 14:17:35 2026-07-16 BK