alvinj

#!/bin/bash

#------------------------------------------------------------------------------
# Name:    sbtmkdirs
# Version: 1.5
# Purpose: Create an SBT project directory structure with a few simple options.
# Author:  Alvin Alexander, http://alvinalexander.com
# License: Creative Commons Attribution-ShareAlike 2.5 Generic
#          http://creativecommons.org/licenses/by-sa/2.5/
#------------------------------------------------------------------------------

agnoster

agnoster.zsh-theme

A ZSH theme optimized for people who use:

• Solarized
• Git
• Unicode-compatible fonts and terminals (I use iTerm2 + Menlo)

For Mac users, I highly recommend iTerm 2 + Solarized Dark

dsparks

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("sna", "ggplot2", "Hmisc", "reshape2")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Empty ggplot2 theme
new_theme_empty <- theme_bw()
new_theme_empty$line <- element_blank()
new_theme_empty$rect <- element_blank()
new_theme_empty$strip.text <- element_blank()

vatsan

{
 "metadata": {
  "name": "PyMADlib"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

wpm

from pyspark import SparkContext

import numpy as np

from sklearn.cross_validation import train_test_split, Bootstrap
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

def run(sc):

widged

chartFactory

/affini-tech/ChartFactory

Based on D3.JS and Dimple, ChartFactory provide the ability to build quickly D3.JS charts without coding any lines of javascript. Just define your dashboard in a JSON and voila !

charts: [
        {id:'chart1',
         width:800,height:250,

xAxis:{type:'Category',field: "Month",orderRule:'Date'},

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

kashif

from __future__ import print_function

from keras.datasets import cifar10
from keras.layers import merge, Input
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils