| curiousily/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras |
289 |
|
0 |
0 |
almost 7 years ago |
0 |
|
5 |
mit |
Jupyter Notebook |
| iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data |
| gqcn/gkvdb |
110 |
|
0 |
0 |
about 7 years ago |
1 |
March 03, 2019 |
2 |
mit |
Go |
| [mirror] Go语言开发的基于DRH(Deep-Re-Hash)深度哈希分区算法的高性能高可用Key-Value嵌入式事务数据库。基于纯Go语言实现，具有优异的跨平台性，良好的高可用及文件IO复用设计，高效的底层数据库文件操作性能，支持原子操作、批量操作、事务操作、多表操作、多表事务、随机遍历等特性。 |
| awslabs/fraud-detection-using-machine-learning |
98 |
|
0 |
0 |
almost 5 years ago |
0 |
|
2 |
apache-2.0 |
Jupyter Notebook |
| Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker |
| wulalago/LearningNote |
33 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
|
|
| some resources on my path in deep learning and medical image analysis |
| mbchang/decentralized-rl |
26 |
|
0 |
0 |
over 4 years ago |
0 |
|
3 |
mit |
Python |
| Decentralized Reinforcment Learning: Global Decision-Making via Local Economic Transactions (ICML 2020) |
| sharmaroshan/Fraud-Detection-in-Online-Transactions |
23 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
gpl-3.0 |
Jupyter Notebook |
| Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting |
| variationalkk/User-and-Entity-Behavior-Analytics-UEBA |
18 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
mit |
Python |
| User and Entity Behavior Analytics by deep learning |
| Das00130/Anti-Money-Laundering-using-Keras |
12 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Utilize the deep learning library Keras to classify transactions as fraudulent(1) or non-fraudulent(0). |
| Jingyi-Luo/Stock_Price_Movement_Prediction_RNN_CNN_FFNN |
12 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using transaction data from the Limit Order Book (LOB). |
| robkinyon/dbm-deep |
11 |
|
0 |
0 |
over 8 years ago |
0 |
|
5 |
|
Perl |
| DBM::Deep Perl module |
