Training Data for Machine Learning: Human Supervision from Annotation to Data Science

✍ Scribed by Anthony Sarkis

Publisher: O'Reilly Media
Year: 2022
Tongue: English
Leaves: 250
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

📜 SIMILAR VOLUMES

Training Data for Machine Learning: Huma

📁 Training Data for Machine Learning: Human Supervision from Annotation to Data Science

✍ Anthony Sarkis 📂 Library 📅 2023 🏛 O'Reilly Media 🌐 English

Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace

Training Data for Machine Learning: Huma

📁 Training Data for Machine Learning: Human Supervision from Annotation to Data Science (Seventh release)

✍ Anthony Sarkis 📂 Library 📅 2023 🏛 O'Reilly Media, Inc. 🌐 English

our 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

Training Data for Machine Learning: Huma

📁 Training Data for Machine Learning: Human Supervision from Annotation to Data Science (8th Early release)

✍ Anthony Sarkis 📂 Library 📅 2023 🏛 O'Reilly Media, Inc. 🌐 English

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 th

Training Data for Machine Learning

📁 Training Data for Machine Learning

✍ Anthony Sarkis 📂 Library 📅 2023 🏛 O'Reilly Media 🌐 English

Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you

Data Science e Machine Learning: Dai dat

📁 Data Science e Machine Learning: Dai dati alla conoscenza

✍ Michele di Nuzzo 📂 Library 📅 2021 🏛 Michele di Nuzzo 🌐 Italian

Estrarre conoscenza dalle informazioni attraverso l'analisi dei dati: quella del data scientist è stata definita la professione più attraente del XXI secolo. Analizzare le relazioni tra i dati, scoprire nuove informazioni e, con l'aiuto del machine learning, sfruttare l'enorme po

Supervised and Unsupervised Learning for

📁 Supervised and Unsupervised Learning for Data Science

✍ Michael W. Berry, Azlinah Mohamed, Bee Wah Yap 📂 Library 📅 2020 🏛 Springer International Publishing 🌐 English

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a