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Search results for jupyter notebook anomaly detection

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124 search results found

Pycaret ⭐ 8,130

An open-source, low-code machine learning library in Python

Loglizer ⭐ 1,062

A log analysis toolkit for automated anomaly detection [ISSRE'16]

Getting Things Done With Pytorch ⭐ 873

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

Telemanom ⭐ 793

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Datastream.io ⭐ 761

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Handson Unsupervised Learning ⭐ 604

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

Generativemodels ⭐ 445

MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications

2018 Machinelearning Lectures Esa ⭐ 324

Machine Learning Lectures at the European Space Agency (ESA) in 2018

Oodsurvey ⭐ 294

The Official Repository for "Generalized OOD Detection: A Survey"

Credit Card Fraud Detection Using Autoencoders In Keras ⭐ 289

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

Log Anomaly Detector ⭐ 278

Log Anomaly Detection - Machine learning to detect abnormal events logs

Extended Isolation Forest for Anomaly Detection

Promotes development of ML algorithms for early detection and classification of undesirable events in offshore oil wells.

Machine Learning ⭐ 196

从零基础开始机器学习之旅

Deep Learning For Hackers ⭐ 196

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Lstm_anomaly_thesis ⭐ 191

Anomaly detection for temporal data using LSTMs

Pwpae Concept Drift Detection And Adaptation ⭐ 175

Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

Machine_learning_2018 ⭐ 146

Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz

Repo 2019 ⭐ 135

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

Vae Lstm For Anomaly Detection ⭐ 128

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Humanbehaviorbku ⭐ 116

Abnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction

Mantranet Pytorch ⭐ 101

Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Anomaly Detection ⭐ 100

Anomaly detection algorithm implementation in Python

Cyber Security ⭐ 89

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

Practicalmachinelearning ⭐ 79

A curated collection of machine learning resources, including notebooks, code, and books, all of which are either free or open-source

Keras Oneclassanomalydetection ⭐ 79

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

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.

Anomatools ⭐ 71

Toolbox for anomaly detection.

Anomaly Reactionrl ⭐ 69

Using RL for anomaly detection in NSL-KDD

Video Anomaly Detection ⭐ 67

Anomaly detection in videos using deep learning.

Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)

Machine Learning Netflow ⭐ 62

Ipython notebook that illustrates effectiveness of machine learning algorithms in anomaly detection of netflow data (inbound/outbound DDoS, etc...)

Fedot.industrial ⭐ 62

Python framework for automated time series classification, object recognition and anomaly detection

Anomaly detection on images using features from pretrained neural networks.

anomaly detection with GAN

Predictive Maintenance ⭐ 56

Datasets for Predictive Maintenance

Ood Detection Using Oecc ⭐ 45

Outlier Exposure with Confidence Control for Out-of-Distribution Detection

Fraud Detection Papers ⭐ 45

Learning Fraud Detection from research papers and industry applications.

Oasw Concept Drift Detection And Adaptation ⭐ 42

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

Anomaly_transformer_pytorch ⭐ 42

PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Detection In Networks Using Machine Learning ⭐ 40

A thesis submitted for the degree of Master of Science in Computer Networks and Security

Anomaly_detection ⭐ 39

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

An open-source unsupervised time-series anomaly detection package by Getcontact Data Team

multi-dimensional, multi-sensor, multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Tilitools ⭐ 33

[ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output prediction

Tsad Model Selection ⭐ 32

Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.

Anomaly_tuning ⭐ 29

Learning hyperparameters for unsupervised anomaly detection

Machine Learning Course at IIT Bhilai

Python codes to implement Q-Net, a meta-learning method for few shot medical image segmentation

models and evaluation framework for trending topics detection

Package for Time Series Forecasting and Anomaly Detection Problems.

Ellipticplusplus ⭐ 26

Elliptic++ Dataset: A Graph Network of Bitcoin Blockchain Transactions and Wallet Addresses

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

A tool for anomaly detection over streaming data based on sentiment analysis

Natural Synthetic Anomalies ⭐ 25

Code accompanying ECCV 2022 paper "Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization".

Autobrewml ⭐ 24

With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.

Singular Spectrum Transformation ⭐ 24

fast implementation of singular spectrum transformation (change point detection algorithm)

Beginning Anomaly Detection Using Python Based Dl ⭐ 23

Source Code for 'Beginning Anomaly Detection Using Python-Based Deep Learning' by Sridhar Alla and Suman Kalyan Adari

Kaspersky_hackathon ⭐ 23

https://events.kaspersky.com/hackathon/

Anomaly Xray Gans ⭐ 22

Anomaly detection on X-Ray MURA dataset using GAN.

Reinforcement Learning for Anomaly Detection

Exp Anomaly Detector ⭐ 21

The design of a time series anomaly detector through RNN and RL.

2018aicity_mcprl ⭐ 20

F Anogan_with_pytorch ⭐ 20

Pidforest ⭐ 19

Code for the PIDForest algorithm for anomaly detection

Holt Winters ⭐ 19

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.

Nasa Jet Engine Maintenance ⭐ 19

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Coursera Deeplearning.ai Stanford University Machine Learning Specialization ⭐ 18

This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.

Ml Based Anomaly Detection ⭐ 18

Spectral Residual

Machine Learning ⭐ 17

A set of jupyter notebooks

Autoencoder_vs_metriclearning ⭐ 17

AutoEncoder vs Metric Learning for Anomaly Detection

Faster Grad Cam ⭐ 16

Faster and more precisely than Grad-CAM

Pydata2016 ⭐ 16

A couple projects using scikit-learn illustrating project decision making.

a time series anomaly detection method based on the calibrated one-class classifier

[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

Pydetect ⭐ 15

Change point and anomaly detections for time-series

Deepanomalydetection_benchmark ⭐ 15

Benchmark for DeepLearning anomaly detection

Efficient_gan Based_anomaly_detection_keras ⭐ 14

Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Transfertools ⭐ 13

Python toolbox for transfer learning.

Msana Online Data Stream Analytics And Concept Drift Adaptation ⭐ 13

Data stream analytics: Implement online learning methods to address concept drift in dynamic data streams. Code for the paper entitled "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems" published in IEEE Transactions on Industrial Informatics.

Unsupervised Anomaly Detection for X-Ray Images

Model Selection for Anomaly Detection in Time Series

Ml_tutorials ⭐ 12

Applied Machine Learning Tutorials

Network Intrusion Detection System ⭐ 12

En este proyecto se evalúan y comparan diferentes técnicas de aprendizaje automático para la detección de intrusiones en red.

Feature Engineering For Fraud Detection ⭐ 12

Implementation of feature engineering from Feature engineering strategies for credit card fraud

PyTorch implementation of paper: adVAE: a Self-adversarial Variational Autoencoder with Gaussian Anomaly Prior Knowledge for Anomaly Detection

Gan Yhat ⭐ 12

GAN implementations

Usad On Wadi And Swat ⭐ 12

Unofficial implementation of the KDD2020 paper "USAD: UnSupervised Anomaly Detection on multivariate time series" on two datasets cited in the papers, "SWaT" (Secure Water Treatment) and "WADI" (Water Distribution)

ZTF anomaly detection

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

Realseries ⭐ 9

RealSeries is a comprehensive out-of-the-box Python toolkit for various tasks, including Anomaly Detection, Granger causality and Forecast with Uncertainty, of dealing with Time Series Datasets.

A Python library for Bayesian Anomaly Detection

Tf2 Ganomaly ⭐ 9

Tensorflow2 implementation of the paper GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Replication_arxiv_1805_03735 ⭐ 8

Replication files for arXiv:1805.03735 Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic

Cic Ddos2019 Deeplearning ⭐ 8

🛡️ A GRU deep learning system against attacks in Software Defined Networks (SDN).

Clustering Based Anomaly Detection ⭐ 8

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

Machine_learning ⭐ 8

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