mosquera lucy (15 risultati)

Condizione: very_good.

Condizione: good. Book has corner edge dings and or scratches and signs of light wear.

Condizione: New.

Paperback. Condizione: New. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data generated from real data-so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenueData scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In.

Paperback. Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Brand New. 151 pages. 9.25x7.00x0.50 inches. In Stock.

Paperback. Condizione: New. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data generated from real data-so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenueData scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure.

Condizione: Muy bueno. : ¡Viva la Vuelta! es un libro que explora la historia y la leyenda de la Vuelta a España, una de las competiciones ciclistas más importantes del mundo. A través de sus 488 páginas, los autores Adrian Bell, Bernat López López, Lucy Fallon y Albino Santos Mosquera nos sumergen en los momentos más emocionantes y las anécdotas más curiosas de esta emblemática carrera. Publicado por Cultura Ciclista, este libro en tapa blanda es una lectura imprescindible para los amantes del ciclismo y la historia del deporte español. EAN: 9788494189869 Tipo: Libros Categoría: Deportes y Recreación|Historia Título: ¡Viva la Vuelta! Autor: Adrian Bell| Bernat López López| Lucy Fallon| Albino Santos Mosquera Idioma: es-ES Páginas: 488 Formato: tapa blanda.

Paperback. Condizione: New. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data generated from real data-so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenueData scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure.

Condizione: New. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data so you can perform se.

Paperback. Condizione: New. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data generated from real data-so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenueData scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure.