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hehuanshu96's Projects

datasciencer icon datasciencer

a curated list of R tutorials for Data Science, NLP and Machine Learning

dbda-python icon dbda-python

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python code

dcc icon dcc

This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

deep-learning-papers icon deep-learning-papers

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.

demos icon demos

Demos for several data science tools, products and services that SparkIQ Labs uses.

distribute_crawler icon distribute_crawler

使用scrapy,redis, mongodb,graphite实现的一个分布式网络爬虫,底层存储mongodb集群,分布式使用redis实现,爬虫状态显示使用graphite实现

dou_ban_spider icon dou_ban_spider

A Simple spider that use to crawl the douban Top 100 moive name and input all list

doubanspiders icon doubanspiders

豆瓣电影、书籍、小组、相册、东西等爬虫集 writen in Python

econ5121a icon econ5121a

lecture notes for Econ5121A: Econometrics I. This is an open-source writing project.

econ5170 icon econ5170

Econ 5170 @CUHK: Computational Methods in Economics (2017 Spring)

econometrics icon econometrics

Exercises of Introductory Econometric A Modern Approach 6th Wooldridge with R and Python

enterprise-registration-data-of-chinese-mainland icon enterprise-registration-data-of-chinese-mainland

**大陆 31 个省份1978 年至 2019 年一千多万工商企业注册信息,包含企业名称、注册地址、统一社会信用代码、地区、注册日期、经营范围、法人代表、注册资金、企业类型等详细资料。This repository is an dataset of over 10,000,000 enterprise registration data of 31 provinces in Chinese mainland from 1978 to 2019.【工商大数据】、【企业信息】、【enterprise registration data】。

ents icon ents

R implementation of maximum entropy synthetic controls

facerank icon facerank

FaceRank - Rank Face by CNN Model based on TensorFlow.FaceRank-人脸打分基于 TensorFlow 的 CNN 模型。

fast-transx icon fast-transx

An Efficient implementation of TransE and its extended models for Knowledge Representation Learning

fes icon fes

Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson

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