pkindom's starred repositories
python_pygame_plant_vs_zoomie_game_from_scratch
python_pygame_plant_vs_zoomie_game_from_scratch
time-series-analysis
Collection of notebooks for time series analysis
baby-llama2-chinese
用于从头预训练+SFT一个小参数量的中文LLaMa2的仓库；24G单卡即可运行得到一个具备简单中文问答能力的chat-llama2.
Awesome-Autonomous-Driving
awesome-autonomous-driving
CrossFormer
The official code for the paper: https://openreview.net/forum?id=_PHymLIxuI
Anomaly-Transformer
About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
SSA_python
麻雀搜索算法(Sparrow Search Algorithm, SSA)的python实现
Swarm-intelligence-optimization-algorithm
种群算法复现(swarm-algorithm)，包括乌鸦搜索(Crow Search Algorithm, CSA)、樽海鞘群算法(Salp Swarm Algorithm, SSA)、缎蓝园丁鸟优化算法(Satin Bowerbird Optimizer, SBO)、麻雀搜索算法(Sparrow Search Algorithm, SSA)、 狼群搜索算法(2007WPS, 2013WPA)、正余弦优化算法(Sine Cosine Algorithm, CSA）、烟花算法(Fireworks Algorithm, FA)
Electricity_Demand_and_Price_forecasting
Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction
train_arch
cnn+rnn+attention: vgg(vgg16,vgg19)+rnn(LSTM, GRU)+attention, resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152)+rnnrnn(LSTM, GRU)+attention, inception_v4+rnn(LSTM, GRU)+attention, inception_resnet_v2+rnn(LSTM, GRU)+attention,..... vgg(vgg16,vgg19), resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152), inception_v4, inception_resnet_v2,.....
Relation-Extraction
Attention-based bidirectional GRU and CNN for relation classification
CNN-BiGRU-Attention
CNN-BiGRU-Attention模型
f-se-lstm-time-series
本项目是论文《F-SE-LSTM: A Time Series Anomaly Detection Method Based on Frequency Domain Information》的实验代码，实现了多种时间序列异常检测模型，并构建一个异常检测方法。
abnormal-detection
时间序列异常检测常用模型实现
mc-lstm-time-series
本项目是论文《Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series》的实验代码，实现了多种时间序列异常检测模型。
TSAD_Evaluator
一个用于评估各类时间序列异常检测(Time Series Anomaly Detection) 算法的框架
Aiops-Learning-Resources
收集AIOPS（智能运维），时间序列，异常检测，关联分析，告警收敛，根因分析，数据挖掘，机器学习，深度学习的学习资源。欢迎star。Collect learning resources for AIOPS (Intelligent Operation and Maintenance), time series, anomaly detection, correlation analysis, alarm convergence, root cause analysis, data mining, machine learning, and deep learning. Welcome star.
1kuang_datas
亿矿云大数据处理框架：借助Hadoop、Spark、Storm等分布式处理架构，满足海量数据的批处理和流处理计算需求。 亿矿云大数据预处理：运用数据冗余剔除、异常检测、归一化等方法对原始数据进行清洗，为后续存储、管理与分析提高质量数据来源。 亿矿云大数据存储与管理：通过分布式文件系统、NoSQL数据库、关系数据库、时序数据库等不同的数据管理引擎实现海量工业数据的分区选择、存储、编目与索引等。
Flow-Bandwidth-System
毕设项目 异常流量识别和带宽检测系统
network_anomaly_detection
基于Django Restframework的异常检测系统，分析服务为Spark SQL和Spark Mllib，每天通过自动跑定时job从全量数据中导入正常数据供算法模型训
awesome-public-datasets
A topic-centric list of HQ open datasets.