henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

ashrithr

Introduction to Kafka

Kafka acts as a kind of write-ahead log (WAL) that records messages to a persistent store (disk) and allows subscribers to read and apply these changes to their own stores in a system appropriate time-frame.

Terminology:

• Producers send messages to brokers
• Consumers read messages from brokers
• Messages are sent to a topic

timothyandrew

Introduction

• This script lets you set up and use a temporary DigitalOcean droplet to download torrent files.
• Once downloaded, they can be streamed down to your local machine.
• This uses transmission-cli for the torrent client, and nginx to serve files.

Setup on Local Machine

• This assumes that you have a DigitalOcean account and tugboat set up, as well as seedbox-setup.sh present in the current directory.

fulmicoton

import itertools
from collections import defaultdict, OrderedDict
from math import log

def dcg(scores):
    return sum( (2**score - 1) / log(i+2) for (i, score) in enumerate(scores) )

class Session(object):

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

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

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

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)

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

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.