CryptonetsCryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Stars: ✭ 152 (-11.11%)
LibraErgonomic machine learning for everyone.
Stars: ✭ 1,925 (+1025.73%)
MarianaThe Cutest Deep Learning Framework which is also a wonderful Declarative Language
Stars: ✭ 151 (-11.7%)
GhactionsGitHub actions for R and accompanying R package
Stars: ✭ 159 (-7.02%)
Benchm MlA minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Stars: ✭ 1,835 (+973.1%)
AvalancheAvalanche: a End-to-End Library for Continual Learning.
Stars: ✭ 151 (-11.7%)
LearnpythonforresearchThis repository provides everything you need to get started with Python for (social science) research.
Stars: ✭ 163 (-4.68%)
Gluon TsProbabilistic time series modeling in Python
Stars: ✭ 2,373 (+1287.72%)
Capsule Net Pytorch[NO MAINTENANCE INTENDED] A PyTorch implementation of CapsNet architecture in the NIPS 2017 paper "Dynamic Routing Between Capsules".
Stars: ✭ 158 (-7.6%)
TestovoeHome assignments for data science positions
Stars: ✭ 149 (-12.87%)
NeuralLATEX: TikZ package for drawing neural networks. Also available on CTAN at http://www.ctan.org/tex-archive/graphics/pgf/contrib/neuralnetwork
Stars: ✭ 169 (-1.17%)
Project kojakTraining a Neural Network to Detect Gestures and Control Smart Home Devices with OpenCV in Python
Stars: ✭ 147 (-14.04%)
Ml Hub🧰 Multi-user development platform for machine learning teams. Simple to setup within minutes.
Stars: ✭ 148 (-13.45%)
Nyc TransportA Unified Database of NYC transport (subway, taxi/Uber, and citibike) data.
Stars: ✭ 148 (-13.45%)
Sweetie DataThis repo contains logstash of various honeypots
Stars: ✭ 163 (-4.68%)
FastbookThe fastai book, published as Jupyter Notebooks
Stars: ✭ 13,998 (+8085.96%)
Learn PythonPython Top 45 Articles of 2017
Stars: ✭ 148 (-13.45%)
Textfeatures👷♂️ A simple package for extracting useful features from character objects 👷♀️
Stars: ✭ 148 (-13.45%)
GensimTopic Modelling for Humans
Stars: ✭ 12,763 (+7363.74%)
EvalmlEvalML is an AutoML library written in python.
Stars: ✭ 145 (-15.2%)
FedmsgFederated Messaging with ZeroMQ
Stars: ✭ 165 (-3.51%)
Awesome AiA curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project)
Stars: ✭ 161 (-5.85%)
Merlin.jlDeep Learning for Julia
Stars: ✭ 147 (-14.04%)
Awesome Pytorch ListA comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Stars: ✭ 12,475 (+7195.32%)
DatacompyPandas and Spark DataFrame comparison for humans
Stars: ✭ 147 (-14.04%)
PycwtA Python module for continuous wavelet spectral analysis. It includes a collection of routines for wavelet transform and statistical analysis via FFT algorithm. In addition, the module also includes cross-wavelet transforms, wavelet coherence tests and sample scripts.
Stars: ✭ 146 (-14.62%)
Bookstore📚 Notebook storage and publishing workflows for the masses
Stars: ✭ 162 (-5.26%)
FrvsrFrame-Recurrent Video Super-Resolution (official repository)
Stars: ✭ 157 (-8.19%)
Fantasy Basketball Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.
Stars: ✭ 146 (-14.62%)
Selfie2animeAnime2Selfie Backend Services - Lambda, Queue, API Gateway and traffic processing
Stars: ✭ 146 (-14.62%)
GeniA Clojure dataframe library that runs on Spark
Stars: ✭ 152 (-11.11%)
100daysofmlcodeMy journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge.
Stars: ✭ 146 (-14.62%)
Shape Detection🟣 Object detection of abstract shapes with neural networks
Stars: ✭ 170 (-0.58%)
MatplotplusplusMatplot++: A C++ Graphics Library for Data Visualization 📊🗾
Stars: ✭ 2,433 (+1322.81%)
HandoutTurn Python scripts into handouts with Markdown and figures
Stars: ✭ 1,973 (+1053.8%)
PresentationsSlide show presentations regarding data driven investing.
Stars: ✭ 162 (-5.26%)
Pygm🐍 Python library implementing sorted containers with state-of-the-art query performance and compressed memory usage
Stars: ✭ 156 (-8.77%)
Docker tutorialCode and helper scripts for article on Medium "How Docker Can Help You Become A More Effective Data Scientist"
Stars: ✭ 145 (-15.2%)
ZigzagPython library for identifying the peaks and valleys of a time series.
Stars: ✭ 156 (-8.77%)
TextbookPrinciples and Techniques of Data Science, the textbook for Data 100 at UC Berkeley
Stars: ✭ 145 (-15.2%)
Py RseResearch Software Engineering with Python course material
Stars: ✭ 145 (-15.2%)
LazynlpLibrary to scrape and clean web pages to create massive datasets.
Stars: ✭ 1,985 (+1060.82%)
TscvTime Series Cross-Validation -- an extension for scikit-learn
Stars: ✭ 145 (-15.2%)
Scipy con 2019Tutorial Sessions for SciPy Con 2019
Stars: ✭ 142 (-16.96%)
BatchflowBatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Stars: ✭ 156 (-8.77%)
BoostarootaA fast xgboost feature selection algorithm
Stars: ✭ 165 (-3.51%)
Datascience Pizza🍕 Repositório para juntar informações sobre materiais de estudo em análise de dados e áreas afins, empresas que trabalham com dados e dicionário de conceitos
Stars: ✭ 2,043 (+1094.74%)
Bodywork CoreDeploy machine learning projects developed in Python, to Kubernetes. Accelerated MLOps 🚀
Stars: ✭ 145 (-15.2%)
NettackImplementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".
Stars: ✭ 156 (-8.77%)