privacy VS tf2-published-models

Compare privacy vs tf2-published-models and see what are their differences.

privacy

Library for training machine learning models with privacy for training data (by tensorflow)

tf2-published-models

Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard. (by sarus-tech)
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privacy tf2-published-models
2 1
1,874 38
0.7% -
7.8 0.0
7 days ago over 2 years ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

privacy

Posts with mentions or reviews of privacy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-17.

tf2-published-models

Posts with mentions or reviews of tf2-published-models. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-17.

What are some alternatives?

When comparing privacy and tf2-published-models you can also consider the following projects:

differential-privacy - Google's differential privacy libraries.

tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow

GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

dp-xgboost

EnvisEdge - Deploy recommendation engines with Edge Computing

Differential-Privacy-Guide - Differential Privacy Guide

adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams