NARINPARK

NARINPARK's Stars

• jbrownlee/Datasets

  Machine learning datasets used in tutorials on MachineLearningMastery.com

  1.1k1.5k

• WGUNDERWOOD/motifcluster

  Motif-based weighted spectral clustering in Python, R and Julia

  Language:Python263

• YanzhangIloveme/Adaptive-Feature-Weighted-K-Means

  This algorithm is based on the paper 'K-Means clustering algorithm Based on Adapative Feature Weighted'

  Language:Python287

• JS-Choi513/Predicting-the-Housing-Price-Index-in-Changwon-City-Using-the-LSTM-Model

  다변량 시계열 자료를 학습한 LSTM 모델을 활용하여 창원시 지역구별(의창구, 성산구, 마산합포구, 마산회원구, 진해구) 주택가격지수 예측

  Language:Jupyter Notebook11

• daeunni/Insurance-contest

  2020 미래에셋 금융 빅데이터 페스티벌 (보험금 청구분야 Final 1위 solution)

  Language:Jupyter Notebook1

• cran/tmvtnorm

  :exclamation: This is a read-only mirror of the CRAN R package repository. tmvtnorm — Truncated Multivariate Normal and Student t Distribution. Homepage: https://www.r-project.org

  Language:R67

• ralphma1203/trun_mvnt

  Language:Python43

• david-cortes/approxcdf

  (Python, R, C) Fast approximations for the CDF of multivariate normal distributions

  Language:C++185

• eminorhan/mixture-of-experts

  Mixture of experts layers for Keras

  Language:Python9316

• AmazaspShumik/mtlearn

  Multi-Task Learning package built with tensorflow 2 (Multi-Gate Mixture of Experts, Cross-Stitch, Ucertainty Weighting)

  Language:Python515

• krishnakalyan3/MixtureOfExperts

  Master Thesis. Code written in python. (Keras with Tensorflow backend)

  Language:Python2110

• GitiHubi/deepAI

  Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

  Language:Jupyter Notebook20384

• curiousily/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras

  iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

  Language:Jupyter Notebook528282

• K-imlab/Classification_of_oil_condition

  Language:Jupyter Notebook3

• Sidz1812/Anomaly_Detection

  Unsupervised machine learning models that can detect outliers / anomalies in real network data

  Language:Jupyter Notebook1

• johnckim/NYC_Property_Anomaly_Detection

  Unsupervised model to find potential real estate frauds in NYC property data | project at USC

  Language:Jupyter Notebook1

• ledio7/ADBench

  Benchmark for unsupervised learning algorithms applied to anomaly detection

  Language:Python2

• GuansongPang/ADRepository-Anomaly-detection-datasets

  ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

  38059

• krisskul/mvem

  Maximum likelihood parameter estimation in multivariate distributions using EM algorithms

  Language:Python51

• SvenSerneels/mvskew

  Multivariate skew distribution samplers

  Language:Python5

• PolinaKirichenko/EM

  Language:Python5

• kaist-dmlab/ARCUS

  Language:Python256

• greentfrapp/tensorflow-dagmm

  Tensorflow reproduction of DAGMM

  Language:Python6

• shasure/tf-dagmm-kdd

  DAGMM Tensorflow implementation with KDD99

  Language:Jupyter Notebook34

• Valiant719/EM-algorithm-GMM-tensorflow

  Language:Python42

• KangaroosInAntarcitica/mixes

  A mixture models package including GMM, Skew GMM, GMN and DGMM

  Language:Jupyter Notebook5

• hoya012/awesome-anomaly-detection

  A curated list of awesome anomaly detection resources

  2.8k517

• jinh0park/Autoencoders-tf2.0

  TensorFlow 2.0 implementations of various autoencoders.

  Language:Python166

• viettra-xai/S-DAGMM

  DAGMM for Outlier Detection and Pretraining Technique

  Language:Jupyter Notebook2

• stefansturlu/FederatedMedical

  Language:Python92