Human in the loop machine learning : active learning and annotation for human-centered AI / Robert (Munro) Monarch, foreword by Christopher D. Manning.

Por:

Monarch, Robert (Munro) [autor]

Colaborador(es):

Manning, Christopher D [autor de contenido textual suplementario]

Tipo de material: Texto

texto

Tipo de medio:

sin mediación

Tipo de soporte:

volumen

ISBN:

9781617296741

Tema(s):

Clasificación LoC:

Q 325.5 M66.2021

Resumen: This book lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

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Notas de título ( 2 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura topográfica	Copia número	Estado	Fecha de vencimiento	Código de barras
Libros	Biblioteca Francisco Xavier Clavigero Acervo	Acervo General	Q 325.5 M66.2021 (Navegar estantería(Abre debajo))	ej. 1	Disponible		UIA207775

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Previo	Q 320 G7a.1978 Cibernética sin matemáticas /	Q 320 G7a.1978 Cibernética sin matemáticas /	Q 325 M32 Machine learning : an artificial intelligence approach /	Q 325.5 M66.2021 Human in the loop machine learning : active learning and annotation for human-centered AI /	Q 325.75 H37.2017 The elements of statistical learning : data mining, inference, and prediction /	Q 334 J64 Pattern recognition and artifical intelligence /	Q 334.7 E36.2020 Artificial intelligence and bioethics /	Siguiente

Incluye índice

This book lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.