seventianyu's Projects
早期Linux实验
learning to protect communications with adversarial neural cryptography
本项目集成了全网优秀的攻防工具项目,包含自动化利用,子域名、敏感目录、端口等扫描,各大中间件,cms漏洞利用工具以及应急响应等资料。
Implementation of calibration bounds for differential privacy in the shuffle model
An index of algorithms for learning causality with data
This repo includes ChatGPT prompt curation to use ChatGPT better.
The author's officially unofficial PyTorch BigGAN implementation.
Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)
My personal website 🤔
The Microsoft Office Word template of BUPT Thesis for Master Degree.
Charm: A Framework for Rapidly Prototyping Cryptosystems
ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型
科研工作专用ChatGPT拓展,特别优化学术Paper润色体验,支持自定义快捷按钮,支持自定义函数插件,支持markdown表格显示,Tex公式双显示,代码显示功能完善,新增本地Python/C++/Go项目树剖析功能/项目源代码自译解能力,新增PDF和Word文献批量总结功能/PDF论文全文翻译功能
潜伏之赤途
计算机学报的latex模板
Data analysis and development of ML solutions for the Arrhythmia Data Set
Code release for ConvNeXt model
Data and code for the CausalNLI dataset paper
Credit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government related credit card transactions over the 2010 calendar year. The data presented a supervised problem as it included a column showing the transaction’s fraud label (whether a transaction was fraudulent or not). It also contained identifying information about each transaction such as the credit card number, merchant, merchant state, etc. The dataset had 96,753 records and 10 data fields. We first described and visualized each of the 10 data fields, cleaned the dataset, and filled in missing values. Then we created many variables and performed feature selection. Finally, we created a variety of machine learning models (both linear and nonlinear) and highlighted our results.
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
在这里面我会记录一下我在机器学习中一步一步的路程,我一定会慢慢努力的哈哈
deeplearning.ai(吴恩达老师的深度学习课程笔记及资源)
Diffusion Models for Causal Discovery
Source code of paper "Differentially Private Generative Adversarial Network"
An encrypted netdisk. Base on KVM(Key Encapsulation Mechanism) with CP-ABE(Cipher Policy Attribute Encryption). Multi attributes-set allowed.
A physical-world attack for face recognition systems
Steps towards physical adversarial attacks on facial recognition
Attribute-based Encryption