ISBN: 9781492072744 - practical synthetic data generation: balancing privacy and the broad availability of data (28 risultati)

Paperback. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Soft cover. Condizione: Very Good. VERY GOOD softcover, no marks in text; a clean, gently used copy. Book.

Paperback. Condizione: Very Good. 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 secondary analysis to do research, understand customer behavior or develop new products.…

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

Paperback. Condizione: Very Good. 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 secondary analysis to do research, understand customer behavior or develop new products.…

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.…

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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback or Softback. Condizione: New. Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. Book.

Condizione: As New. Unread book in perfect condition.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New.

Paperback. Condizione: new. Paperback. 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 distributions Methods 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 relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure" 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 secondary analysis to do research, understand customer behavior or develop new products Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: As New. Unread book in perfect condition.

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

Condizione: New. pp. 175.

Condizione: New. 2020. Paperback. . . . . .

Condizione: New. In English.

Condizione: New. pp. 175.

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.

Condizione: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condizione: 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 datafake data generated from real dataso 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.This book describes:- Steps for generating synthetic data using multivariate normal distributions- Methods 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 relationships- Multiple approaches and metrics you can use to assess data utility- How analysis performed on real data can be replicated with synthetic data- Privacy implications of synthetic data and methods to assess identity disclosure.…

Paperback. Condizione: new. Paperback. 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 distributions Methods 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 relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure" 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 secondary analysis to do research, understand customer behavior or develop new products Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.