Samaraweera dumindu (9 risultati)

Trade Paperback. Condizione: VG-. used trade paperback edition. lightly shelfworn, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Condizione: As New. Unread copy in mint condition.

Condizione: New. Brand New.

Paperback. Condizione: New. Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You'll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning. Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you'll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you're done, you'll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.…

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condizione: New. Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You'll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning. Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you'll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you're done, you'll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.…