louiekang's repositories
APICloud-Studio
APICloud提供的一款开源免费的HTML编码工具，方便开发者进行应用的开发和调试，整个项目基于Eclipse和Aptana，导出的产品详见APICloud-Studio-RCP项目。
canvas
canvas is a simple interface to most common matplotlib functions
DeepLearning
Deep Learning (Python, C/C++, Java, Scala, Go)
dive-into-machine-learning
Dive into Machine Learning with ipython notebook and scikit-learn
FinancialCharting
Stock charts. Application uses ExtJS, HighStock, ServiceStack (with swagger and caching) and Quandl.com (datafeed).
hbase-docker
HBase Dockerfile
java
Java exercise
java-memcached-client
A simple, asynchronous, single-threaded memcached client written in java.
jupyterhub
Multi-user server for Jupyter notebooks
Memcached-Java-Client
Information about this project lives on the wiki
notes-server
Notes demo server
ntm
An implementation of Neural Turing Machines
nupic
Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.
opencms-core
The Java open source content management system by Alkacon Software
pattern
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
phantomjs
PhantomJS 2.0.0 binaries for OSX and linux (temp, until upstream is patched)
Q-learning-NN
Python based Deep CNN Q-Learner for FOREX
quant
Quant is a python-based system for stock trading strategy backtesting
scrapy-stats
Scrapy项目，抓取国家统计局区划代码，并用D3.js可视化
ScrapyWeiboByAPI
基于Scrapy框架，用于抓取新浪微博数据，主要包括微博内容，评论以及用户信息
spark-neo4j
A single docker image that combines Neo4j Mazerunner and Apache Spark GraphX into a powerful all-in-one graph processing engine
spider_news_all
Scrapy Spider for 各种新闻网站
Stanford-parser-Python-Chniese
Python Interface for Stanford parser based on Jpype
stanford_parser_cn_python
on going tools for connect python and stanford parser by pipe.
weibo_scrapy
WEIBO_SCRAPY is a Multi-Threading SINA WEIBO data extraction Framework in Python.
Zhihu_bigdata
使用scrapy和pandas完成对知乎300w用户的数据分析。首先使用scrapy爬取知乎网的300w，用户资料，最后使用pandas对数据进行过滤，找出想要的知乎大牛，并用图表的形式可视化。