samklr

/**
 * Copyright (C) 2009-2013 Typesafe Inc. <http://www.typesafe.com>
 */
package actorbintree


import akka.actor._
import scala.collection.immutable.Queue

samklr

class Cell(row: Int, col: Int) {
  var neighbors: Seq[Cell] = Nil
  var isAlive = false
  def aliveNeighbors = neighbors.filter(n => n.isAlive)

  def isAliveNext = {
    def dies = { println("%s dies!".format(this)); false }
    isAlive match {
      case true => aliveNeighbors.size match {
        case size if (size < 2) => dies

samklr

1. General Background and Overview
  a) Probabilistic Data Structures for Web Analytics and Data Mining : On Highly Scalable Blog (http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/) : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  b) Models and Issues in Data Stream Systems : (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.9846)
  c) Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani (http://www.vldb.org/conf/2002/S10P03.pdf) : One of the early papers on the subject.
  d) Methods for Finding Frequent Items in Data Streams by Graham Cormode & Marios Hadjieleftheriou (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&type=pdf)
  e) The space complexity of approximating the frequency moments by Noga Alon, Yossi Matias, Mario Szegedy : one of the most influential papers introducing succinctness in computing fre

samklr

import org.junit.Before;
import org.junit.Test;
import sun.misc.Unsafe;
import sun.reflect.ReflectionFactory;

import java.lang.reflect.Constructor;
import java.lang.reflect.Field;
import java.lang.reflect.Modifier;

import static org.junit.Assert.assertEquals;

samklr

package org.apache.spark.examples

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.RDD
import java.util.Random
import scala.collection.mutable
import org.apache.spark.serializer.KryoRegistrator
import com.esotericsoftware.kryo.Kryo

samklr

http://deeplearning.net/
http://deeplearning.net/tutorial/ (Good One ... )
http://deeplearning.net/reading-list/tutorials/

Geoffrey Hinton (God himself) Coursera Course
https://www.coursera.org/course/neuralnets

Geoffrey Hinton Academic page with great links
https://www.cs.toronto.edu/~hinton/

samklr

Tuning Storm+Trident

Tuning a dataflow system is easy:

The First Rule of Dataflow Tuning:
* Ensure each stage is always ready to accept records, and
* Deliver each processed record promptly to its destination

samklr

##Google Interview Questions: Product Marketing Manager

• Why do you want to join Google? -- Because I want to create tools for others to learn, for free. I didn't have a lot of money when growing up so I didn't get access to the same books, computers and resources that others had which caused money, I want to help ensure that others can learn on the same playing field regardless of their families wealth status or location.
• What do you know about Google’s product and technology? -- A lot actually, I am a beta tester for numerous products, I use most of the Google tools such as: Search, Gmaill, Drive, Reader, Calendar, G+, YouTube, Web Master Tools, Keyword tools, Analytics etc.
• If you are Product Manager for Google’s Adwords, how do you plan to market this?
• What would you say during an AdWords or AdSense product seminar?
• Who are Google’s competitors, and how does Google compete with them? -- Google competes on numerous fields: --- Search: Baidu, Bing, Duck Duck Go

samklr

package com.grasswire.grasswireurlshortener

import akka.actor.ActorSystem
import com.typesafe.config.ConfigFactory
import org.apache.commons.validator.routines.UrlValidator
import spray.http.StatusCodes
import spray.routing._
import scala.util.Random
import scalaz.concurrent._

samklr

#### Start IPython, generate SHA1 password to use for IPython Notebook server

$ ipython
Python 2.7.5 |Anaconda 1.8.0 (x86_64)| (default, Oct 24 2013, 07:02:20) 
Type "copyright", "credits" or "license" for more information.

IPython 1.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.