Mark Tyneway tynes

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

REMOTE: Set up SSH service on the remote host

Skip if already set up, check with: sudo service ssh status
sudo apt-get install ssh
Edit ssh config file with the lines below: sudo vim /etc/ssh/sshd_config

PermitRootLogin no              #Disable direct login from root
AllowUsers user1 user2 user3    #*Only* allow this users to connect
AllowTcpForwarding yes          #Required to setup the tunnel, yes or commented out
PubkeyAuthentication yes        #Enables public key authentication
Port 22                         #Listening port of the server

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

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

To install unbound you can use homebrew

$ brew install unbound ldns

	package main

	import (
	"fmt"
	"reflect"
	)

	// Name of the struct tag used in examples
	const tagName = "validate"

	These commands are based on a askubuntu answer http://askubuntu.com/a/581497
	To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
	USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
	ABSOLUTELY NO WARRANTY.

	If you are still reading let's carry on with the code.

	sudo apt-get update && \
	sudo apt-get install build-essential software-properties-common -y && \
	sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \

	// Usage:
	// Copy and paste all of this into a debug console window of the "Who is Hiring?" comment thread
	// then use as follows:
	//
	// query(term \| [term, term, ...], term \| [term, term, ...], ...)
	//
	// When arguments are in an array then that means an "or" and when they are seperate that means "and"
	//
	// Term is of the format:
	// ((-)text/RegExp) ( '-' means negation )

	import { Component } from "React";

	export var Enhance = ComposedComponent => class extends Component {
	constructor() {
	this.state = { data: null };
	}
	componentDidMount() {
	this.setState({ data: 'Hello' });
	}
	render() {