hubbucket-team Goto Github PK

covid19_projections

COVID-19 Projections Using Machine Learning

covid_community_profile_report_faq

The FAQ for the Community Profile Report

cpython

The Python programming language

craft-pytorch

Official implementation of Character Region Awareness for Text Detection (CRAFT)

cri-o

Open Container Initiative-based implementation of Kubernetes Container Runtime Interface

cromwellonazure

Microsoft Genomics supported implementation of the Broad Institute's Cromwell workflow engine on Azure

crosscloudci

Integrating, testing, and deploying projects within the CNCF ecosystem. Results published daily to the CNCF CI Dashboard.

crossplane

Quick and reliable way to convert NGINX configurations into JSON and back.

cruise-control

Cruise-control is the first of its kind to fully automate the dynamic workload rebalance and self-healing of a kafka cluster. It provides great value to Kafka users by simplifying the operation of Kafka clusters.

cryptonets

CryptoNets 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.

ct-policy

cudf

GPU Data Frame

cudf-alpha

cuDF [alpha] - RAPIDS Merge of GoAi into cuDF

cudnn.torch

Torch-7 FFI bindings for NVIDIA CuDNN

curator

Curator -- a build and package automation tool

custom-post-type-ui

Admin UI settings for creating custom post types and taxonomies in WordPress

custrings

cvelist

Pilot program for CVE submission through GitHub

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

dalex

Descriptive mAchine Learning EXplanations

dask_gdf

Dask support for distributed GDF object

data-engineering-howto

A list of useful resources to learn Data Engineering from scratch

data-prep-minitoolkit

Three little Python scripts for data preparation: remove commas, add commas, concatenate files

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.

databaseanalysis-

Scripts for analysing stats database. This is a mess right now!

datasets

A collection of datasets ready to use with TensorFlow

deap

Distributed Evolutionary Algorithms in Python

debian-med-links-analysis

A python notebook to keep track of the cross-linking between bio.tools and EDAM

debugger

A JupyterLab debugger UI extension

deck.gl

WebGL2 powered geospatial visualization layers