gununu

/*
    Use, modification and distribution are subject to the Boost Software License,
    Version 1.0. (http://www.boost.org/LICENSE_1_0.txt).
*/

#ifndef GUNUNU_GIL_PREVIEW_HPP
#define GUNUNU_GIL_PREVIEW_HPP

#include <windows.h>
#pragma comment(lib, "user32")

sixtenbe

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

JoshuaEstes

Moved to https://github.com/JoshuaEstes/CheatSheets

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

campagnola

import numpy as np
import zlib
import struct

def make_png(data):
    """
    Convert numpy array to PNG byte array.
    
    *data* must be (H, W, 4) with dtype=ubyte    
    """

atduskgreg

Interactive Machine Learning

Taught by Brad Knox at the MIT Media Lab in 2014. Course website. Lecture and visiting speaker notes.

• Power to the People: The Role of Humans in Interactive Machine Learning by Knox, Cakmak, Kulesza, Amershi, and Lau
• A Few Useful Things to Know about Machine Learning by Domingos
• Machine Learning that Matters by Wagstaff
• Beyond Concise and Colorful: Learning Intelligible Rules by Pazzani et al.
• [Designing Games with a Purpose] (https://www.cs.cmu.edu/~biglou/GWAP_CACM.pdf) by von Ahn and Dabbish
• [Human Model Evaluation in Interactive Supervised

justmiles

# Notes:
# You'll need to update any references to 'module.vpc' with your VPC's info (subnets, security groups, etc)
# If you define your subnets as a map, this will deploy different replicas across availabity zones. Note 
# that the master and first replica will be in the same AZ.
#
# Look through the 'connection' fields and update with your bastion_host/key combos
#

# Number of IPA Replica(s) to deploy
variable "ipa_replicas" {

MMesch

#!/usr/bin/env python
"""
Robust B-Spline regression with scikit-learn
"""

import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate as si
from sklearn.base import TransformerMixin
from sklearn.pipeline import make_pipeline

ChrisDowning

ClusterName=pearc20
ControlMachine=ip-192-168-0-100

SlurmUser=slurm
SlurmctldPort=6817
SlurmdPort=6818
AuthType=auth/munge
StateSaveLocation=/var/spool/slurm/ctld
SlurmdSpoolDir=/var/spool/slurm/d
SwitchType=switch/none

ChrisDowning

#!/bin/bash

export SLURM_ROOT=/etc/slurm
export CLUSTER_REGION=$(curl -sS http://169.254.169.254/latest/dynamic/instance-identity/document | jq -r '.region')
export SLURM_POWER_LOG=$SLURM_ROOT/power_save.log
export PATH=$PATH:/usr/local/bin:/usr/bin

function aws_shutdown()
{
    COMPUTE_INSTANCE=$(aws ec2 describe-instances \