differential-privacy-library
Keras
differential-privacy-library | Keras | |
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2 | 78 | |
779 | 61,044 | |
1.3% | 0.5% | |
4.8 | 9.9 | |
9 days ago | about 23 hours ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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differential-privacy-library
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Well, crackers.
Differential privacy. Basically i wanted to create a randomly generated database file, akin to medical records, create a Private Aggregation of Teacher Ensembles algorithms based on 20-60% of its content and then use this teacher model on the other 80-40% of database which was just a plaintext, not that that matters. The problem is, I've barely got ideas on how it all works, and the one example I've found used Cryptonumeric's library called cn.protect. And that went like I've already described. I've fallen back on practical part of the paper and found another way of getting any practical usage as the assignment requires and now am trying to use https://github.com/IBM/differential-privacy-library and the example on 30s guide to instead make the practical part about choosing epsilon ( a measure of how much information you can give away as a result of one query on the database to a third malicious party) by tracking associated accuracy of result dataset compared to original. I hope I'll manage to edit the code to accept my text file after parsing it through into ndarray from txt, separating the last column to use as a target and going from there.
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Differential Privacy project on Python
IBM's Diffprivlib is a well-documented implementation of differential privacy in Python. Source code and getting started documentation is available on the IBM differential-privacy-library Github repository.
Keras
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Library for Machine learning and quantum computing
Keras
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My Favorite DevTools to Build AI/ML Applications!
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
- Release: Keras 3.3.0
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Getting Started with Gemma Models
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
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Keras 3.0
All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467
You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/
- Keras 3: A new multi-back end Keras
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Can someone explain how keras code gets into the Tensorflow package?
I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
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How popular are libraries in each technology
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.
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List of AI-Models
Click to Learn more...
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Official Question Thread! Ask /r/photography anything you want to know about photography or cameras! Don't be shy! Newbies welcome!
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.
What are some alternatives?
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
PyDP - The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
scikit-learn - scikit-learn: machine learning in Python
awesome-machine-unlearning - Awesome Machine Unlearning (A Survey of Machine Unlearning)
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
fides - The Privacy Engineering & Compliance Framework
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
PrivacyEngCollabSpace - Privacy Engineering Collaboration Space
tensorflow - An Open Source Machine Learning Framework for Everyone
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.