A framework for Privacy Preserving Machine Learning
MIT License
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Published by marksibrahim over 4 years ago
Initial release to PyPI: https://pypi.org/project/crypten/
Source for paper "Attacking Binarized Neural Networks"
Python Cryptographic (File Locking) Library
Python 3.9 to JavaScript compiler - Lean, fast, open!
Debugging, monitoring and visualization for Python Machine Learning and Data Science
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crac...
A library for doing homomorphic encryption operations on tensors
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Codebase for Image Classification Research, written in PyTorch.
TensorLy: Tensor Learning in Python.
A toolbox to iNNvestigate neural networks' predictions!
Biologically based dynamics for machine learning and deep learning tools for neuroscience.
cryptography is a package designed to expose cryptographic primitives and recipes to Python devel...
Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to tradition...
TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Bac...
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed conf...