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Probabilistic machine learning : an introduction / Kevin P. Murphy.

Por:

Murphy, Kevin P [Autor]

Idioma: Inglés Series Adaptive computation and machine learning seriesEditor: Cambridge, Massachusetts : The MIT Press, 2022Descripción: xxix, 826 pages : illustrations (some color) ; 24 cmTipo de contenido:

texto

Tipo de medio:

no mediado

Tipo de soporte:

volumen

ISBN:

9780262046824

Tema(s):

Clasificación CDD:

006.31 M954p 23

Recursos en línea:

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Resumen: "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"--

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura topográfica	Estado	Notas	Fecha de vencimiento	Código de barras	Reserva de ítems
Recursos Electrónicos	Sede Quito	Col General	006.31 M954p (Navegar estantería(Abre debajo))	Disponible	Disponible en Biblioteca Digital PUCE
Libro	Sede Quito Primer piso	Col General	006.31 M954p (Navegar estantería(Abre debajo))	Disponible			PUCE212265

Total de reservas: 0

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Includes bibliographical references and index.

"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"--

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