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buaa-dl2021

BUAA-2021深度学习中作业

concept-drift

Algorithms for detecting changes from a data stream.

concept_drift_on_streaming_data

Cocnept drift evaluation over time series in streaming data on a industrial use case.. Data is generated using a digital twin generated from a small sample data. Twin is deployed ona flask endpoint and then a data simualtor instantiates twin to generate data from three work stations. Data and its features are stored on real time, an airflow server orchestrates concept drift evaluation on a batch process and results might be seen onreal time on a dash front.

conceptdriftresources

A collection of resources for concept drift data and software

dmvst-net

DMVST-Net for AAAI 2018

dmvst_net

包括部分数据预处理以及基于Tensorflow的DMVST_Net模型的实现

drifter

Concept Drift and Concept Shift Detection for Predictive Models

graphneuralnetwork

The learning of the GraphNeuralNetwork

graphneuralnetwork-1

《深入浅出图神经网络：GNN原理解析》配套代码

leedeeprl-notes

李宏毅《深度强化学习》笔记，在线阅读地址：https://datawhalechina.github.io/leedeeprl-notes/

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

models

Models and examples built with TensorFlow

oasw-concept-drift-detection-and-adaptation

Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" accepted in IEEE Internet of Things Magazine.

online-machine-learning

Implementation of an online learning algorithm to do classification under concept drift

planetoid

Semi-supervised learning with graph embeddings

spatiotemporal-model

存储时空数据预测的相关模型，基于tensorflow 2.0 实现

taxi-demand

taxi-demand prediction model using deep learning

traffic-forecasting