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text_classification

all kinds of text classificaiton models and more with deep learning

tgboost

Tiny Gradient Boosting Tree

tianchi-diabetes

天池精准医疗大赛——人工智能辅助糖尿病遗传风险预测 第一赛季

tianchi-diabetes-top12

tianchi-diabetic_predict

天池精准医疗大赛——人工智能辅助糖尿病遗传风险预测, 代码及总结

tianchi-industry-ai

天池工业AI大赛

tianchi_competition_diabetes

天池-人工智能辅助糖尿病预测Rank42/2522

tianchi_diabate

天池大数据，糖尿病预测初赛

tianchi_disease_session_1

天池糖尿病预测模型_未进复赛_

tianchi_power

Applying machine learning to predict the future power usage of factories.

tianci_disease_prediction

天池 糖尿病遗传预测

timeseriesprediction

天池智慧交通预测挑战赛解决方案

timit_tools

tools around preparing TIMIT for HMM (with HTK) and deep learning (with Theano) methods

trainticket

开源一个简单的刷火车票的python脚本，网络请求容错，并且余票时会有音乐提醒

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

ustc-cs-courses-resource

:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)

vowpal_wabbit

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

weaklysupervisedlearning

Weakly supervised learning framework for classification.

wechat

A High Copy WeChat ,SNS APP (高仿微信)

wechat-applet

微信小程序项目

wechat_jump_game

python 微信《跳一跳》辅助

wechat_wegoing

微信小游戏《星途wegoing》刷分代码

wechatsogou

基于搜狗微信搜索的微信公众号爬虫接口

writer-tutorial

PyQt text editor tutorial repository

xblog

A powerful and responsive blog system powered by laravel 5.5.

xgboost

Tutorial how to use xgboost

xgboost-1

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

xiamiforlinuxproject

Xiami For Linux Project

xlearn

High Performance, Easy-to-use, and Scalable Machine Learning Package (C++, Python, R)

yandexcatboost-python-demo

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset