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Sam Zeitlin szeitlin

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# Credit http://stackoverflow.com/a/2514279
for branch in `git branch -r | grep -v HEAD`;do echo -e `git show --format="%ci %cr" $branch | head -n 1` \\t$branch; done | sort -r
#!/bin/bash
OPENSSL_VERSION="1.0.2c"
curl -O http://www.openssl.org/source/openssl-$OPENSSL_VERSION.tar.gz
tar -xvzf openssl-$OPENSSL_VERSION.tar.gz
mv openssl-$OPENSSL_VERSION openssl_i386
tar -xvzf openssl-$OPENSSL_VERSION.tar.gz
mv openssl-$OPENSSL_VERSION openssl_x86_64
cd openssl_i386
@szeitlin
szeitlin / infection_graphgist.adoc
Last active August 29, 2015 14:16
Infectious Enthusiasm for Graphs

Mapping Infected Users On Khan Academy


Introduction

Using a graph model to represent Users on the site, we can test what happens when we roll out changes so that associated Users will all see the same version of the site. In other words, we can make changes such that one User gets 'infected' with a new version of the site, and some or all of their connected Users will receive that same version.

@szeitlin
szeitlin / about.md
Last active August 29, 2015 14:16 — forked from antichris/about.md

Fork your own Gist

This is a bookmarklet that adds a fully functional Fork button to your own Gist.

If a Fork button is already present in the page, this bookmarklet will set focus to it instead of adding another one.

The change is temporary and the button will disappear as soon as you navigate away from that Gist (clicking the Fork button does this for you as well).


@szeitlin
szeitlin / k-NN.adoc
Last active August 29, 2015 14:06
forkable version

TraitSync k-Nearest Neighbors and Cosine Similarity

Introduction

Using k-nearest neighbors, similarities are calculated between each element in the data set using some distance / similarity metric ^[1]^ that the researcher chooses (there are many distance / similarity metrics), where the distance / similarity between any two elements is calculated based on the two elements' attributes. A data element’s k-NN are the k closest data elements according to this distance / similarity.


1. A distance metric measures distance; the higher the distance the further apart the neighbors. A similarity metric measures similarity; the higher the similarity the closer the neighbors.