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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por O'Reilly Media 6/9/2020, 2020
ISBN 10: 1492072745 ISBN 13: 9781492072744
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Añadir al carritoPaperback. Condición: 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.
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Idioma: Inglés
Publicado por O'Reilly Media, Inc, USA, 2020
ISBN 10: 1492072745 ISBN 13: 9781492072744
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Idioma: Inglés
Publicado por Oreilly & Associates Inc, 2020
ISBN 10: 1492072745 ISBN 13: 9781492072744
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Añadir al carritoPaperback. Condición: 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.
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Añadir al carritoCondición: 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.
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por O'reilly Media Jun 2020, 2020
ISBN 10: 1492072745 ISBN 13: 9781492072744
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 67,03
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - 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 revenue. Data 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.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Practical Synthetic Data Generation | Balancing Privacy and the Broad Availability of Data | Khaled El Emam (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | O'Reilly Media | EAN 9781492072744 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por O'Reilly Media, Inc, USA, 2020
ISBN 10: 1492072745 ISBN 13: 9781492072744
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 56,07
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.