seventianyu Goto Github PK

mta_cp_abe

Flexible revocation in ciphertext-policy attribute-based encryption with verifiable ciphertext delegation.

numpy-ml

Machine learning, in numpy

pate

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/1610.05755) [Work in Progress]

patternrecognitionanddeeplearning-lab

2019春哈工大模式识别与深度学习实验

personalized-federated-learning-with-model-quantization

使用模型量化降低通信开销，动态调整量化比特，降低等待时间，提高收敛速度

ppdl_lwe

Implementation of Privacy-Preserving Deep Learning via Additively Homomorphic Encryption (Using LWE schema)

predicting-credit-card-fraud-transactions

# Problem: Predicting Credit Card Fraud ## Introduction to business scenario You work for a multinational bank. There has been a significant increase in the number of customers experiencing credit card fraud over the last few months. A major news outlet even recently published a story about the credit card fraud you and other banks are experiencing. As a response to this situation, you have been tasked to solve part of this problem by leveraging machine learning to identify fraudulent credit card transactions before they have a larger impact on your company. You have been given access to a dataset of past credit card transactions, which you can use to train a machine learning model to predict if transactions are fraudulent or not. ## About this dataset The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred over the course of two days and includes examples of both fraudulent and legitimate transactions. ### Features The dataset contains over 30 numerical features, most of which have undergone principal component analysis (PCA) transformations because of personal privacy issues with the data. The only features that have not been transformed with PCA are 'Time' and 'Amount'. The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction amount. 'Class' is the response or target variable, and it takes a value of '1' in cases of fraud and '0' otherwise. Features: `V1, V2, ... V28`: Principal components obtained with PCA Non-PCA features: - `Time`: Seconds elapsed between each transaction and the first transaction in the dataset, $T_x - t_0$ - `Amount`: Transaction amount; this feature can be used for example-dependent cost-sensitive learning - `Class`: Target variable where `Fraud = 1` and `Not Fraud = 0` ### Dataset attributions Website: https://www.openml.org/d/1597 Twitter: https://twitter.com/dalpozz/status/645542397569593344 Authors: Andrea Dal Pozzolo, Olivier Caelen, and Gianluca Bontempi Source: Credit card fraud detection - June 25, 2015 Official citation: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015. The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

python-fhez

Official mirror of Python-FHEz; Python Fully Homomorphic Encryption (FHE) Library for Encrypted Deep Learning as a Service (EDLaaS).

pytorch-image-classification

用于pytorch的图像分类，包含多种模型方法，比如AlexNet，VGG，GoogleNet，ResNet，DenseNet等等，包含可完整运行的代码。除此之外，也有colab的在线运行代码，可以直接在colab在线运行查看结果。也可以迁移到自己的数据集进行迁移学习。

pytorch-pretrained-biggan

🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

robust-classification

CVPR 2022 Workshop Robust Classification

robustart

The first comprehensive Robustness investigation benchmark on large-scale dataset ImageNet regarding ARchitecture design and Training techniques towards diverse noises.

robustbench

RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]

seventianyu

seventianyu.github.io

seventianyu.github.io-droped-

simple_knn

With python

snippet

just some code snippet

speech2text

语音转文字

stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

stylegan2-ada-pytorch

StyleGAN2-ADA - Official PyTorch implementation

swarm-learning

A simplified library for decentralized, privacy preserving machine learning

synthetic_data_release

tigerbot

TigerBot: A multi-language multi-task LLM

toolbench

An open platform for training, serving, and evaluating large language model for tool learning.

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

tsinghuarss

uagan

Training Federated GANs with Theoretical Guarantees: AUniversal Aggregation Approach

waking-up

计算机基础（计算机网络/操作系统/数据库/Git...）面试问题全面总结，包含详细的follow-up question以及答案；全部采用【问题+追问+答案】的形式，即拿即用，直击互联网大厂面试:rocket:；可用于模拟面试、面试前复习、短期内快速备战面试...