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