Differential Privacy in Machine Learning with TensorFlow Privacy Reviews

KIHYEON J. · Reviewed больше 1 года ago

GEONWOO S. · Reviewed больше 1 года ago

motioner D. · Reviewed больше 1 года ago

정원 문. · Reviewed больше 1 года ago

하 유. · Reviewed больше 1 года ago

용성 이. · Reviewed больше 1 года ago

Charlie K. · Reviewed больше 1 года ago

혁 이. · Reviewed больше 1 года ago

Yuchieh Cheng 鄭宇傑 E. · Reviewed больше 1 года ago

Leslie M. · Reviewed больше 1 года ago

Sohyun K. · Reviewed больше 1 года ago

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Emir S. · Reviewed больше 1 года ago

가현 이. · Reviewed больше 1 года ago

euiseok l. · Reviewed больше 1 года ago

영집 김. · Reviewed больше 1 года ago

Introduction to the topic of Privacy Budget was useful. However, the lab would have been more effective if the lab adopted the following approach: 1) Train the model using Privacy Budget n 2) Test the results 3) Retrain the model using Privacy Budget n+delta 4) Test the results 5) Observe the difference in model behaviour between the model using Privacy Budget n vs the model behaviour using Privacy Budget n+delta This would allow the lab user to observe that a lower privacy budget bounds more tightly an adversary's ability to improve their guess.

Paul C. · Reviewed больше 1 года ago

JONGIL P. · Reviewed больше 1 года ago

선경 윤. · Reviewed больше 1 года ago

Noe G. · Reviewed больше 1 года ago

제형 전. · Reviewed больше 1 года ago

강민영 강. · Reviewed больше 1 года ago

YoonSeok N. · Reviewed больше 1 года ago

Abhishek K. · Reviewed больше 1 года ago

Jaewon C. · Reviewed больше 1 года ago

stophobia G. · Reviewed больше 1 года ago