mengshuhai Goto Github PK

bilibili-backup-1

blog.io

简单直接可用博客模板

category-tree-clustering-python

ciphermed

Implementation of "Machine Learning Classification over Encrypted Data" by Raphael Bost, Raluca Ada Popa, Stephen Tu and Shafi Goldwasser

code

开发者课堂课程资料

compose

Define and run multi-container applications with Docker

computer-networking-a-top-down-approach-notes

《计算机网络－自顶向下方法(原书第6版)》编程作业，Wireshark实验文档的翻译和解答。

cryptonets

an implementation of CryptoNets

cryptonets-1

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.

cs231n.github.io

Public facing notes page

csbook

C# 入门经典6.0

ddia

《设计数据密集型应用》中文翻译 《Designing Data-Intensive Application》

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deeplearning4j

Deep Learning for Java, Scala & Clojure on Hadoop & Spark With GPUs - From Skymind

deeplearningbook-chinese

Deep Learning Book Chinese Translation

dinn

Fast Homomorphic Evaluation of Deep Discretized Neural Networks

dl4j-examples

Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)

drone

Drone is a Continuous Delivery platform built on Docker, written in Go

effective-resourses

:book:学习资源整合

fasttext

Library for fast text representation and classification.

fhe

Implementation of the DGHV fully homomorphic encryption scheme

game

gin

Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.

gitskills

he-transformer

nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph

heide

helib

An Implementation of homomorphic encryption

httprouter

A high performance HTTP request router that scales well

interview-notebook

:books: 技术面试需要掌握的基础知识整理，欢迎编辑~

introneuralnetworks

Introducing neural networks to predict stock prices