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simpleutils

日常用到的一些工具集合

sina-analyze

sklearn_cotraining

An implementation of the Co-Training semi-supervised learning technique from (Blue, Mitchell 1998) that is meant to work well with scikit-learn classifiers.

slackcat

A Slack webhook for injecting cat images from http.cat in response to status codes in messages.

slider

slider-1

smac3

Sequential Model-based Algorithm Configuration

small

A small framework to split app into small parts

smartbird

Mahjong Artificial intelligence,AI, platform for Java

smartd

Smart meter data analytics dashboard

smweb

This is a Operations management system

snownlp

Python library for processing Chinese text

snowplow

Enterprise-strength web and event analytics, powered by Hadoop, Kinesis, Redshift and Postgres

socialcrawler

A simple web crawler for QQ Space that helps analyze social relationships.

spammail

spammail sender

spark-admin

The Spark-Admin provides administrators and developers a GUI to provision and manage Spark clusters easily

spark-jobserver

REST job server for Spark

spark-notes

Deep Dive into Apache Spark 深入研读Spark源码

spark-shell-examples

Spark REPL versions of the examples found on `Spark in Action` (Manning)

sparkinternals

Notes talking about the design and implementation of Apache Spark

speechrecognitionhmm

spider

A configurable web spider with a easy-to-use web console

springbooks

学习后端的相关电子书

sqle

SQLE is a SQL audit platform | SQLE 是一个支持多场景，原生支持 MySQL 审核且数据库类型可扩展的 SQL 审核工具

sqlpad

Web-based SQL editor. Legacy project in maintenance mode.

sreworks

stabilityguide

【稳定大于一切】打造国内稳定性领域知识库，让无法解决的问题少一点点，让世界的确定性多一点点。

stackoverflow-qa-recommendation

This is a simple recommendation system based on collaborative filtering. The goal is to recommend questions to users he might be interested in answering.

stanford-tensorflow-tutorials

This repository contains code examples for the course CS 20SI: TensorFlow for Deep Learning Research.

starfish

Starfish is a self-tuning system for big data analytics. Starfish builds on Hadoop while adapting to user needs and system workloads to provide good performance automatically, without any need for users to understand and manipulate the many tuning knobs in Hadoop.