Mosquera Lucy (15 results)

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

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

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

Condition: New.

Paperback. Condition: 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.…

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

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

Condition: New. pp. 175.

Condition: New. In English.

Condition: New. pp. 175.

Paperback. Condition: 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.…

Condition: 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.