关于“運用 TensorFlow Privacy 在機器學習技術中實現差異化隱私”的评价

Satyam V. · 评论1 hour之前

the lab is unable to monitor the progress. I'm not able to move forward

shlok p. · 评论2 hours之前

Nikitha P. · 评论2 hours之前

youngsuk kum 금. · 评论2 hours之前

Yerrannagari S. · 评论3 hours之前

Akash S. · 评论8 hours之前

Armand A. · 评论9 hours之前

Omkar S. · 评论11 hours之前

Kumari V. · 评论14 hours之前

Jumple P. · 评论16 hours之前

Bunny G. · 评论17 hours之前

Himanshu J. · 评论18 hours之前

Overall Good experience..

Vaidehi D. · 评论18 hours之前

Pratik D. · 评论18 hours之前

PALLAPU L. · 评论19 hours之前

Ashwini S. · 评论20 hours之前

Karthik S. · 评论21 hours之前

The lab environment experienced several library dependency conflicts and encountered issues locating the installation path for the TensorFlow kernel. Despite successfully completing the tasks, the system fails to flag the lab as 'complete' regardless of multiple attempts. Could you please manually mark this as completed in the system? Kind regards and thank you in advance. Output: DP-SGD performed over 60000 examples with 32 examples per iteration, noise multiplier 0.5 for 1 epochs without microbatching, and no bound on number of examples per user. This privacy guarantee protects the release of all model checkpoints in addition to the final model. Example-level DP with add-or-remove-one adjacency at delta = 1e-05 computed with RDP accounting: Epsilon with each example occurring once per epoch: 10.726 Epsilon assuming Poisson sampling (*): 3.800 No user-level privacy guarantee is possible without a bound on the number of examples per user. (*) Poisson sampling is not usually done in training pipelines, but assuming that the data was randomly shuffled, it is believed the actual epsilon should be closer to this value than the conservative assumption of an arbitrary data order.

Enrique Á. · 评论21 hours之前

Noorus S. · 评论21 hours之前

Gayatri C. · 评论22 hours之前

Matteo B. · 评论23 hours之前

Akshaya C. · 评论23 hours之前

Manas P. · 评论23 hours之前

Satish P. · 评论1 day之前

muchos bug para resolver este problema

Francisco José P. · 评论1 day之前