fawkesley

#!/bin/sh

# /etc/NetworkManager/dispatcher.d/pre-up.d/randomize-mac-addresses.sh

# INSTALL
#
# > curl -L 'https://gist.github.com/paulfurley/46e0547ce5c5ea7eabeaef50dbacef3f/raw/56ee5dd5f40dec93b8f7438cbdeda5475ea3b5d2/randomize-mac-addresses.sh' |sudo tee /etc/NetworkManager/dispatcher.d/pre-up.d/randomize-mac-addresses.sh
# > sudo chmod +x /etc/NetworkManager/dispatcher.d/pre-up.d/randomize-mac-addresses.sh

# Configure every saved WiFi connection in NetworkManager with a spoofed MAC

aparrish

imwilsonxu

# -*- coding: utf-8 -*-

from flask import Flask, request, render_template, current_app
from flask_wtf import Form
from wtforms.validators import DataRequired
from wtforms import SelectField, SelectMultipleField, SubmitField


app = Flask(__name__)

kevindavenport

shagunsodhani

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.

jmoiron

all: monte-c monte-go monte-rs monte-gccgo

monte-go:
	go build montepi.go && mv montepi monte-go

monte-rs:
	rustc -O -o monte-rs montepi.rs

monte-c:
	gcc -std=c99 -O2 -o monte-c montepi.c -lm

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

bqm

// Open the developer console of your favorite browser
// on the page of your favorite meetup where you're on the wailist (grrr)
// and paste this by putting your name
// This will actually compute the position in the waitlist div which seems to correspond to the waitlist position
var findPosition = function(username) { return $("#rsvp-list-waitlist h5").map(function(i, el) {return {"pos": i, "name": $(el).text()}}).filter(function(i, el) {return el["name"].indexOf(username) >= 0;})}
findPosition("my displayed name") // where my displayed name is the name that is displayed for you on the meetup

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

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