sczhai

sczhai's Stars

• EthicalML/awesome-production-machine-learning

  A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

  17.7k407782.3k

• ageron/handson-ml3

  A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

  Language:Jupyter Notebook7.9k1431123.2k

• lawlite19/MachineLearning_Python

  机器学习算法python实现

  Language:Python7.3k246202.4k

• MichalDanielDobrzanski/DeepLearningPython

  neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support

  Language:Python2.8k135311.3k

• curiousily/Getting-Things-Done-with-Pytorch

  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 BER

  Language:Jupyter Notebook2.4k5122639

• PacktPublishing/The-Kaggle-Book

  Code Repository for The Kaggle Book, Published by Packt Publishing

  Language:Jupyter Notebook2.2k354455

• cerlymarco/MEDIUM_NoteBook

  Repository containing notebooks of my posts on Medium

  Language:Jupyter Notebook2.1k10251975

• Tencent/Metis

  Metis is a learnware platform in the field of AIOps.

  Language:Python1.7k112100496

• eisman/neo4jd3

  Neo4j graph visualization using D3.js

  Language:JavaScript1.4k7256423

• wangle1218/KBQA-for-Diagnosis

  Knowledge Graph,Question Answering System，基于知识图谱和向量检索的医疗诊断问答系统

  Language:Python1.4k1443383

• chaoming0625/SentimentPolarityAnalysis

  情感极性分析repository1：基于情感词典、k-NN、Bayes、最大熵、SVM的情感极性分析。

  Language:Python1.2k3022336

• JulesBelveze/time-series-autoencoder

  PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

  Language:Python64862463

• imperial-qore/TranAD

  [VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

  Language:Python523110160

• PaddlePaddle/PaddleTS

  Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

  Language:Python49120170119

• marcopeix/TimeSeriesForecastingInPython

  Language:Jupyter Notebook4661012251

• ZhaoZhibin/UDTL

  Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM

  Language:Python4241019106

• omnigeeker/mlnd_distracted_driver_detection

  基于深度学习的驾驶员状态检测，不仅仅可以识别出疲劳驾驶，还能够识别出各种各样的状态

  Language:Jupyter Notebook2766391

• DHI/tsod

  Anomaly Detection for time series data

  Language:Python16371418

• ZhouYuxuanYX/Unsupervised-Deep-Learning-Framework-for-Anomaly-Detection-in-Time-Series-

  Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based) settings for anaomaly detection in time series data

  Language:Python1193234

• AirFin/awesome-TS-anomaly-detection_zh

  1023014

• PatientEz/keras-attention-mechanism

  the extension of https://github.com/philipperemy/keras-attention-mechanism , create a new scipt to add attetion to input dimensions rather than timesteps in the origin project。

  Language:Python782134

• yz-intelligence/AI-Competition

  开源往期获奖竞赛代码

  Language:Python440418

• nicknochnack/Forecasting-Weather-with-Neural-Prophet

  A jupyter notebook showcasing how to forecast temperature using Neural Prophet.

  Language:Jupyter Notebook322221

• Tonystark-001/MedicalKGQA

  基于知识图谱的疾病知识问答系统

  Language:JavaScript251014

• Htring/KGQAHLM

  基于红楼梦知识内容的简易问答系统

  Language:HTML15113

• stxupengyu/GMM-KMeans-for-Outlier-Detection

  针对一维时间序列数据，采用GMM和K-Means算法进行异常点检测。For one-dimensional time series data, GMM and K-means algorithm are used to detect outliers.

  Language:Jupyter Notebook9111

• yeswanthkuruba/Anomaly-Detection-Seq2Seq-RNN

  Anomaly Detection on Time series Data - (uni-variate, multi-variate, multiple-Regression) using Seq2Seq RNN

  Language:Python800

• LiYX-Code/MedicalKGQA

  Language:JavaScript1

• ramannmathur/Anomaly-Detection

  Time-series anomaly detection to monitor the KPI and detect any incident.

  Language:Jupyter Notebook1

• sczhai/TranAD

  [VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

  Language:Python1000