kogakure

*.acn
*.acr
*.alg
*.aux
*.bak
*.bbl
*.bcf
*.blg
*.brf
*.bst

dadoonet

#!/bin/bash

ES='http://localhost:9200'
ESIDX='test3'
ESTYPE='test'

curl -XDELETE $ES/$ESIDX

curl -XPUT $ES/$ESIDX/ -d '{
	"settings" : {

leon

# Upstart script for a play application that binds to an unprivileged user.
# put this into a file like /etc/init/play.conf
#
# This could be the foundation for pushing play apps to the server using something like git-deploy
# By calling service play stop in the restart command and play-start in the restart command.
#
# Usage:
#   start play
#   stop play
#   restart play

starzonmyarmz

/*
 * Yeah, so rake:assets:precompile will fail if this file is ascii, and something
 * it imports is utf-8. To fix it, here is a snowman: ☃ (no, really.)
 */

jtimberman

source "http://rubygems.org"

gem 'berkshelf'

group :integration do
  gem 'test-kitchen', :git => 'git://github.com/opscode/test-kitchen.git', :branch => '1.0'
  gem 'kitchen-ec2', :git => 'git://github.com/opscode/kitchen-ec2.git'
  gem 'kitchen-vagrant', :git => 'git://github.com/opscode/kitchen-vagrant.git'
end

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

e-mon

import os

nltkdata_wn = '/path/to/nltk_data/corpora/wordnet/'
wn31 = "http://wordnetcode.princeton.edu/wn3.1.dict.tar.gz"
if not os.path.exists(nltkdata_wn+'wn3.0'):
    os.mkdir(nltkdata_wn+'wn3.0')
os.system('mv '+nltkdata_wn+"* "+nltkdata_wn+"wn3.0/")
if not os.path.exists('wn3.1.dict.tar.gz'):
    os.system('wget '+wn31)
os.system("tar zxf wn3.1.dict.tar.gz -C "+nltkdata_wn)

graphific

#!/usr/bin/env bash
# Installation script for Deep Learning Libraries on Ubuntu 14.04, by Roelof Pieters (@graphific)
# BSD License

orig_executor="$(whoami)"
if [ "$(whoami)" == "root" ]; then
  echo "running as root, please run as user you want to have stuff installed as"
  exit 1
fi
###################################

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats