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2014

Official content for the Fall 2014 Harvard CS109 Data Science course

adctrprediction

ai-edu

AI education materials for Chinese students, teachers and IT professionals.

analytics

The Analytics Edge. This is the MITx: 15.071x The Analytics Edge offered in the Spring Semester 2014 through edx.org.

appdocs

Application Performance Optimization Summary

architecture.of.internet-product

互联网公司技术架构，微信/淘宝/腾讯/阿里/美团点评/百度/微博/Google/Facebook/Amazon/eBay的架构，欢迎PR补充

aspect-based-sentiment-analysis

A paper list for aspect based sentiment analysis.

awesome-flask

A curated list of awesome Flask resources and plugins

awesome-python

A curated list of awesome Python frameworks, libraries and software. Inspired by awesome-php.

baby_android

婴儿项目Android代码

basic_sentiment_analysis

Code of the blog post: http://fjavieralba.com/basic-sentiment-analysis-with-python.html

beautypickdemo

A collection of beautiful pictures of small tools, based on iOS platform

big-data-resources

大数据/数据挖掘/推荐系统/机器学习相关资源

breakoutdetection

Breakout Detection via Robust E-Statistics

causalimpact

An R package for causal inference in time series

chinese-annotator

Annotator for Chinese Text Corpus (under development, welcome for ideas)

chinesenlpcorpus

搜集、整理、发布 中文 自然语言处理 语料/数据集，与 有志之士 共同 促进 中文 自然语言处理 的 发展。

clouds-lab

Laboratory Material for the course on Cloud Computing

cluedatasetsearch

搜索所有中文NLP数据集，附常用英文NLP数据集

code

Douban CODE

code_py

A collection of well-commented code snippits for data science

cxxnet

CXXNET, yet another neural network toolkit

cyou

c you

dachuwang

大厨网

data-science-45min-intros

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

datagotham2013

datascience-anthology-pydata

PyData, The Complete Works of

deeplearningbibliography

Bibliography for Publications about Deep Learning using GPU

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.