arturmkrtchyan

<script>
    document.addEventListener("DOMContentLoaded", function () {

        /* Intercept into Airtable call */
        window['records'] = [];
        $.ajaxSetup({
            dataFilter: function (data, type) {

arturmkrtchyan

part 1

<script>
document.addEventListener("DOMContentLoaded", function() {
    $('[id=sw-email-capture-submit-btn]').click(function() {
          if($('.sw-input-invalid').length) {
              return;
          }

arturmkrtchyan

    function loadVimeoThumbnail(url, callback){
        $.ajax({
            type:'GET',
            url: 'https://vimeo.com/api/oembed.json?url=' + encodeURIComponent(url),
            dataType: 'json',
            success: callback
        });
    }

    function isVimeo(url) {

arturmkrtchyan

<script>
    document.addEventListener("DOMContentLoaded", function () {

        const emailFieldName = 'Email';
        const jobIdFieldName = 'JobID';
        const formId = 'submitreferral';

        const userEmail = getUserEmail();
        const recordId = getUrlParam('recordId');

arturmkrtchyan

import jdk.incubator.http.*;
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder(URI.create("http://www.google.com")).GET().build();
HttpResponse response = client.send(request, HttpResponse.BodyHandler.asString())
System.out.println(response.body())

arturmkrtchyan

Machine Learning Algorithm families

Supervised Learning (e.g. classification, anomaly detection, regression)

• Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
• A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
• Example problems are classification and regression.
• Example algorithms include Logistic Regression and the Back Propagation Neural Network.

Unsupervised Learning (e.g. clustering and dimensionality reduction)

• Input data is not labelled and does not have a known result.

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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

arturmkrtchyan

curl http://spark-cluster-ip:6066/v1/submissions/status/driver-20151008145126-0000

arturmkrtchyan

10.64.200.48
10.64.200.49
10.64.200.50
10.64.200.51

arturmkrtchyan

#include <pthread.h>
#include <stdlib.h>
#include <unistd.h>

void *thread_function(void *arg) {
  int i;
  for ( i=0; i<20; i++ ) {
    printf("Thread says hi!\n");
    sleep(1);
  }