mubarak

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release candidate 1.2.0rc0
Based on:
    - PyTorch example from Justin Johnson:
      https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c
Required packages: tensorflow (v1.2)
You can install the release candidate 1.2.0rc0 here:
https://www.tensorflow.org/versions/r1.2/install/
Download the weights trained on ImageNet for VGG:

mubarak

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

mubarak

##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

mubarak

import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorCompletionService;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;

import org.apache.commons.httpclient.HttpClient;

mubarak

/**
 * Copyright (C) 2009-2014 Typesafe Inc. <http://www.typesafe.com>
 */
package akka.contrib.mailbox

import scala.concurrent.duration._
import java.util.concurrent.atomic.AtomicInteger
import java.util.concurrent.atomic.AtomicLong
import com.typesafe.config.Config
import akka.actor.{ ActorContext, ActorRef, ActorSystem, ExtendedActorSystem }

mubarak

import akka.actor.Actor

// defining messages -- Object = singleton, case class = DTO (sorta, generally)
object Born
case class Evolve(numberOfNeighbors: Int)
object Status

// receive is the method that handles incoming messages.
// alive: Receive and dead: Receive are defining alternative ways to respond to messages
// these will be switched on during runtime

mubarak

package com.liveperson.predictivedialer.common.sessions

import scala.concurrent.duration.FiniteDuration

/**
 * User: michaelna
 * Date: 1/15/14
 * Time: 2:58 PM
 */
object InviteResponse {

mubarak

package com.mypackage.benchmark

import com.mypackage.util.Logging
import net.lag.configgy.Configgy
import scala.collection.mutable
import scala.collection.JavaConversions._

import java.net.InetSocketAddress
import java.nio.charset.Charset
import java.util.concurrent._

mubarak

As compiled by Kevin Wright a.k.a @thecoda

(executive producer of the movie, and I didn't even know it... clever huh?)

please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!

This gist will be updated as and when I find new information. So it's probably best not to fork it, or you'll miss the updates!

Monday June 16th

mubarak

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