关于“使用 TensorFlow Privacy 在机器学习中应用差分隐私”的评价

Eduardo M. · 已于 8 months前审核

easy

Antonio S. · 已于 8 months前审核

Bruna A. · 已于 8 months前审核

DJ N. · 已于 8 months前审核

Nicholas W. · 已于 8 months前审核

Ronaldo M. · 已于 8 months前审核

Leung S. · 已于 8 months前审核

Fernando M. · 已于 8 months前审核

Daniel Sidnei M. · 已于 8 months前审核

Piotr B. · 已于 8 months前审核

Samruddhi K. · 已于 8 months前审核

Sneha M. · 已于 8 months前审核

Poojitha Reddy S. · 已于 8 months前审核

N Harsha Vardhan R. · 已于 8 months前审核

Tarun K. · 已于 8 months前审核

Regina B. · 已于 8 months前审核

mauro p. · 已于 8 months前审核

I don't feel the lab demonstrated the concept of Differential Privacy well enough. Specifically, it seems that the core of the lab is the function "compute_dp_sgd_privacy.compute_dp_sgd_privacy_statement", in that case the lab should have focused on it and on different reports it provides. Trianing the model wasn't really necessary at all in order to call that function.

Yuval W. · 已于 8 months前审核

Janani D. · 已于 8 months前审核

Joanna S. · 已于 8 months前审核

AMIT G. · 已于 8 months前审核

Abhishek Kothekar 2. · 已于 8 months前审核

Gleison M. · 已于 8 months前审核

Riyaz A. · 已于 8 months前审核

Hojung J. · 已于 8 months前审核