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algorithm-note

该系列包括数组，链表，树，图，递归，DP，有序表等相关数据结构与算法的讲解及代码实现。

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

autoencoder

v1

awesome-ensemble-learning

Ensemble learning related books, papers, videos, and toolboxes

bigdataai

介绍Liao Wenzhe 的一些主要代表作品和收集的一些资料,包括AIOPS，异常检测，根因分析，告警降噪，关联分析，数据安全，数据挖掘，机器学习，深度学习，文本匹配等。

classification-ad

Repository for the paper "Rethinking Assumptions in Anomaly Detection"

coursera-ml-using-matlab-python

coursera吴恩达机器学习课程作业自写Python版本+Matlab原版

cvdd-pytorch

A PyTorch implementation of Context Vector Data Description (CVDD), a method for Anomaly Detection on text.

deep-sad-pytorch

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

deep-svdd

Repository for the Deep One-Class Classification ICML 2018 paper

deep-svdd-pytorch

A PyTorch implementation of the Deep SVDD anomaly detection method

elki

ELKI Data Mining Toolkit

gradio

Create UIs for your machine learning model in Python in 3 minutes

graph-based-selective-outlier-ensembles

Graph based selective outlier ensembles

heu_kms_activator

lscp

Supplementary material for SDM 19 paper "LSCP: Locally Selective Combination in Parallel Outlier Ensembles"

machine_learning_from_scrach

Machine learning algorithms step by step with explanation

mixture-autoencoder

Implementation of "Deep Unsupervised Clustering Using Mixture of Autoencoders"

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

nilm-papers-with-code

An archive for NILM papers with source code and other supplemental material

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

pyod

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

python_for_data_analysis_2nd_chinese_version

《利用Python进行数据分析·第2版》

pytorch_geometric

Graph Neural Network Library for PyTorch

seq2point-nilm

Sequence-to-point learning for non-intrusive load monitoring