jmxhhyx

jmxhhyx's Stars

• shenweichen/DeepCTR

  Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

  Language:Python7.6k1783702.2k

• bojone/bert4keras

  keras implement of transformers for humans

  Language:Python5.4k72490926

• shenweichen/GraphEmbedding

  Implementation and experiments of graph embedding algorithms.

  Language:Python3.8k64661k

• hexiangnan/attentional_factorization_machine

  TensorFlow Implementation of Attentional Factorization Machine

  Language:Python4091526153

• shenweichen/Bytedance_ICME2019_challenge_baseline

  Bytedance_ICME2019_challenge_baseline

  Language:Python1454835

• ShusenTang/BDC2019

  2019**高校计算机大赛——大数据挑战赛 第三名解决方案

  Language:Jupyter Notebook1246226

• jiuxianghedonglu/Context-Aware-Multi-Modal-Transportation-Recommendation

  KDD CUP 2019 Baseline

  Language:Python1013125

• SunnyMarkLiu/datacastle_travel_services_predict-rank5

  第二届智慧中国杯精品旅行服务成单预测 Rank 5（AUC 0.97539）解决方案

  Language:Jupyter Notebook672037

• carrie0307/baidu-ee2020

  2020语言与智能技术竞赛：事件抽取任务 -- 联合抽取baseline

  Language:Python54177

• gaozhanfire/CCF-BDCI2019_Entity_Discovery

  CCF-BDCI大数据与计算智能大赛-互联网金融新实体发现-9th

  Language:Python541314

• jmxhhyx/DCIC-The-Estimation-of-SCADA-Data-Loss-in-Offshore-Wind-Farm

  We extract actual SCADA data of a certain offshore wind farm and artificially remove some of the data, including but not limited to deleting all data of a certain time period, data of a certain turbine at a certain time, some fields of a certain turbine at a certain time, etc. The applicant needs to use the remaining data to recover the deleted data, and the accuracy of the recovery shall be the final evaluation benchmark

  Language:Jupyter Notebook42027

• ouwenjie03/sohucampus2019

  sohucampus2019 baseline

  Language:Python282112

• EliasCai/bytedance-icme

  code for 2019 bytedance icme

  Language:Python16422

• jmxhhyx/aaaaa

  Traditional approaches to production equipment maintenance remain mainly in corrective maintenance and routine-based maintenance. Corrective maintenance, which is no fail no repair, implies unscheduled downtime of equipment, severe security risk and unacceptable economic loss, while routine or time-based maintenance is time consuming and costly. By data analysis and modeling, predictive maintenance forecasts the residual life or possible failure of key components, and schedules maintenance accordingly，in order to avoid unscheduled downtime and to save cost.

  Language:Jupyter Notebook15114

• ksboy/lic2020-ee

  Language:Python13111

• Mywayking/XF2019_APP_Classify

  讯飞大数据应用分类标注挑战赛深度学习模型Baseline

  Language:Python11111

• impltrait/Bert-NER

  基于bert 命名实体识别

  Language:Python9002

• zyxdSTU/BDCI2019_Financial_Entities

  CCF大数据与计算智能大赛BDCI2019,互联网金融新实体发现赛道baseline, 线上成绩0.277

  Language:Python1100