BarryZM's Stars
LearningOS/rust-based-os-comp2023
2023秋冬季开源操作系统训练营
catboost/tutorials
CatBoost tutorials repository
TencentGameMate/chinese_speech_pretrain
chinese speech pretrained models
aialgorithm/Blog
Python机器学习算法技术博客,有原创干货!有code实践! 【更多内容敬请关注公众号 "算法进阶"】
oinsd/FastAPI-Learning-Example
FastAPI Learning Example,对应中文视频学习教程:https://space.bilibili.com/396891097
shibing624/dialogbot
dialogbot, provide search-based dialogue, task-based dialogue and generative dialogue model. 对话机器人,基于问答型对话、任务型对话、聊天型对话等模型实现,支持网络检索问答,领域知识问答,任务引导问答,闲聊问答,开箱即用。
JINGEWU/Stock-Market-Trend-Analysis-Using-HMM-LSTM
Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory
dusty-nv/jetson-voice
ASR/NLP/TTS deep learning inference library for NVIDIA Jetson using PyTorch and TensorRT
yuxiaowww/BDCI-2018-Supply-Chain-Demand-Forecast
初赛Rank1 复赛Rank1 2018 CCF 大数据与计算智能大赛 供应链需求预测 Miracccccccle
bettenW/2018-iFLYTEK-Marketing-Algorithms-Competition-Finals-Rank1
2018科大讯飞营销算法大赛(冠军方案)
1061700625/RaspberryPi-MagicMirror
基于树莓派的智能魔镜,支持人脸识别、情感监测、热词唤醒、语音交互,以及与手机APP交互、温湿度/新闻热点/日期等的显示 等
WangliLin/xunfei2021_car_loan_top1
2021科大讯飞-车辆贷款违约预测挑战赛 Top1方案
wj19971997/YIZHIFU2020-top1
2020翼支付风险用户识别 初赛、复赛AB榜Rank1
Modo0202/FintechRiskControlModeling
Python金融大数据风控建模实战:基于机器学习源代码
dasiki/Dialog-System-with-Task-Retrieval-and-Seq2seq
京东/淘宝客服对话数据公开,seq2seq生成模型设计对话系统获第二名
aoguai/HumManBot
兼容 GPT2、Bloom 等 Pytorch 框架下的语言模型、人工智能标记语言 (AIML) 和任务型对话系统 (Task) 的深度中文智能对话机器人框架
DataArk/CHIP2021-Task1-Top1
CHIP2021医学对话临床发现阴阳性判别任务冠军方案
rooki3ray/2021BytedanceSecurityAICompetition_track1
2021字节跳动安全AI挑战赛赛道一亚军—— 基于文本和多模态数据的风险识别 题目名称:色情导流用户识别
cXPromise/2021ECAA_Top2_Solution
首届电子商务AI算法大赛TOP2开源代码
huanghepijiu/-
配电网负荷预测,BP神经网络,Cart决策树,GDBT,CatBoost
1superman/risk_model_report
风控建模评分报告
liangzihao8301/Fraud-Detection-in-Insurance-Claims
保险反欺诈预测
shidqiet/xgboost-triton-ray
Deployment of XGBoost model using triton server (FIL backend) and Ray Serve
rickyxume/Data_Statistic_Analysis
2021数据统计与分析大赛全国一等奖方案
blacksevenzqj/zoubo
evelyncy96/Spam-Email-Detection
I built several classification models to detect whether an email is spam or not. I built models and found the best performance using Nested CV and GridSearch under two scenarios: Accuracy-Based & Cost-Sensitive Based. The models I used including Support Vector Classifier/ XGBoost/ LightGBM/ Deep Neural Network/ Decision Tree/ Logistic Regression
PumpkimW/lgb_learn
lgb_model learing
Timehsw/Python-AI
amine-akrout/mental_health_risk
Training and deploying LightGBM Model with MLFlow, fastapi, App Engine and github actions
njsdias/ML_Deploy