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#!/usr/bin/python

import requests, json, pprint, time, socket

CARBON_SERVER = '0.0.0.0'
CARBON_PORT = 2003

def send_msg(message):
    #print 'sending message: %s' % message
    sock = socket.socket()

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public class PalindromeMaker {
	public String make(String baseString) throws Exception{
		int[] hash= new int[26];
		for(int i=0;i<baseString.length();i++){
			char ch=baseString.charAt(i);
			if(ch<'A' || ch>'Z') throw new Exception("Wrong Input");
			hash[ch-'A']++;
		}
		boolean check=false;
		StringBuilder begin=new StringBuilder();

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import sys
import colorsys
from colorz import colorz

WALLPAPER = '/home/james/.wallpaper'
COLORS = '/home/james/.colors'
XRESOURCES = '/home/james/.Xresources'

cols = ''
xres = """

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The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

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1. General Background and Overview

• 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.
• Models and Issues in Data Stream Systems
• 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
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

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Source: http://christonard.com/12-free-data-mining-books/

• An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie & Tibshirani – This book is fantastic and has helped me quite a bit. It provides an overview of several methods, along with the R code for how to complete them. 426 Pages.
• The Elements of Statistical Learning by Hastie, Tibshirani & Friedman – This is an in-depth overview of methods, complete with theory, derivations & code. I’d definitely consider this a graduate level text. I’d also consider it one of the best books available on the topic of data mining. 745 Pages.
• A Programmer’s Guide to Data Mining by Ron Zacharski – This one is an online book, each chapter downloadable as a PDF. It’s also still in progress, with chapters being added a few times each year. Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilson – This book is absolutely fantastic. The author explains Bayesian statistics, provides several diverse examples of how to a

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Beyond Web 2.0 - Django and Python in the modern web ecosystem

Django is over 10 years old; but the web that it was built for - the world of "Web 2.0" - doesn't really exist any more. Yes, we still need to rapidly develop database-backed websites, AJAX-enabled, but the modern web is faced with new problems, and new challenges. Many of those challenges involve interacting with devices that aren't desktop machines, and platforms that aren't a traditional browser - places where Python isn't a first-class citizen.

Does this mean we have to abandon Django and Python for other frameworks and languages? Can we keep using Python and Django on the server side and interact with independent client side frameworks? Or can we push Python and Django into these new environments?

In this talk, Dr Russell Keith-Magee will explore the problems, and some potential solutions, to these problems.

Detailed abstract

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Installing CartoDB on Ubuntu Server 12.04

Get CartoDB Rails application source

sudo apt-get update

sudo apt-get install git

cd ~

git clone --recursive https://github.com/CartoDB/cartodb20.git

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Efficient Estimation of Word Representations in Vector Space

Introduction

• Introduces techniques to learn word vectors from large text datasets.
• Can be used to find similar words (semantically, syntactically, etc).
• Link to the paper
• Link to open source implementation

Model Architecture