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wang_shuang's Projects

book icon book

MyBatis 从入门到精通

boottest icon boottest

Spring boot 集成MyBatis(采用通用Mapper)以及PageHelper分页插件

chainer-regnet icon chainer-regnet

Chainer implementation of RegNet: Multimodal sensor registration using deep neural networks (with simplifications)

complexpytorch icon complexpytorch

A high-level toolbox for using complex valued neural networks in PyTorch

conv-gcn icon conv-gcn

Code for Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit

cryptdb icon cryptdb

A database system that can process SQL queries over encrypted data.

cryptocode icon cryptocode

LaTeX package for typesetting pseudocode and cryptographic games

csbook icon csbook

计算机类常用电子书整理,并且附带下载链接,包括Java,Python,Linux,Go,C,C++,数据结构与算法,人工智能,计算机基础,面试,设计模式,数据库,前端等书籍

deep-learning-in-production icon deep-learning-in-production

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

deeplearningpython icon deeplearningpython

neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support

fhe-mp-cnn icon fhe-mp-cnn

Implementation of deep ResNet model on CKKS scheme in Microsoft SEAL library using multiplexed parallel convolution

gcn icon gcn

Implementation of Graph Convolutional Networks in TensorFlow

gcn-npec icon gcn-npec

PyTorch implementation of GCN-NPEC in "Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach"

gcn-tffc icon gcn-tffc

比较 TCN、GRU、GCN、TGCN、 TCN+GCN 在 交通流量预测方面的准确率效果。

gcn_classification icon gcn_classification

Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval

ids-cnn icon ids-cnn

使用卷积神经网络进行网络入侵检测,正确率可达99.5%

java-deeplearning icon java-deeplearning

java实现的深度学习相关的算法,目前实现了矩阵的运算,包含基本运算,转置,求逆,迹运算,范数,行列式,余子式(刚刚接触深度学习,所以希望能边学边做)

javaee icon javaee

🔥⭐️👍框架(SSM/SSH)学习笔记

kdd-cup-99-cnn-pytorch icon kdd-cup-99-cnn-pytorch

This is a classification model with five classes (normal, DOS, R2L, U2R,PROBING). Ignore the content features of TCP connection ( columns 10-22 of KDD Cup 99 dataset) when training the model to adapt the project that a kdd99 feature extractor

kddcup99-cnn icon kddcup99-cnn

Using PyTorch to train kddcup99 dataset with convolutional neural networks.

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