Athan kgryte

An example of a Multi Layer Perception (MLP) Artificial Neural Network (ANN) for reading handwritten numbers (OCR).

From Fabrice Bellard, with minor name change (umulh):

// return the high 32 bit part of the 64 bit addition of (hi0, lo0) and (hi1, lo1)
Math.iaddh(lo0, hi0, lo1, hi1)

// return the high 32 bit part of the 64 bit subtraction of (hi0, lo0) and (hi1, lo1)
Math.isubh(lo0, hi0, lo1, hi1)

// return the high 32 bit part of the signed 64 bit product of the 32 bit numbers a and b

The final result: require() any module on npm in your browser console with browserify

This article is written to explain how the above gif works in the chrome (and other) browser consoles. A quick disclaimer: this whole thing is a huge hack, it shouldn't be used for anything seriously, and there are probably much better ways of accomplishing the same.

Update: There are much better ways of accomplishing the same, and the script has been updated to use a much simpler method pulling directly from browserify-cdn. See this thread for details: mathisonian/requirify#5

inspiration

Deprecated. See https://www.polymer-project.org/articles/unit-testing-elements.html for the latest version.

Unit Testing Polymer Elements

Note: this guide is a work-in-progress and will be added to the Polymer docs when it's ready. We have updated <seed-element> to include unit tests and this guide has been moved to Google docs. Expect a version on the Polymer site before the end of September.

After spending days working on your <super-awesome> Polymer element, you’re finally ready to share it with the rest of the world. You add the code for using it to your demo, iterate on it over time and come back to it one day when..uh oh. The demo broke because something has gone horribly wrong. Suddenly, <super-awesome> isn’t starting to look so great. Now you’re stuck trying to backtrack through your commit log to figure out how you broke the code. You’re not going to have a fun time.

If you’ve been working on the front-end for a while, even if you haven’t really played with Polymer elements before, this s

	#include <stdio.h>
	#include <stdlib.h>
	#include <sys/time.h>

	//#define ITERS 1000000
	void dgemv_(char , int, int , double, double, int, double, int, double, double, int*);
	int main(int argc, char * argv[])
	{

	int m, n;

	# Hello, and welcome to makefile basics.
	#
	# You will learn why `make` is so great, and why, despite its "weird" syntax,
	# it is actually a highly expressive, efficient, and powerful way to build
	# programs.
	#
	# Once you're done here, go to
	# http://www.gnu.org/software/make/manual/make.html
	# to learn SOOOO much more.

	var vm = require('vm'),
	Contextify = require('contextify'),
	code = 'var square = n * n;',
	fn = new Function('n', code),
	script = vm.createScript(code);

	n = 5;

	benchmark = function(title, funk) {
	var end, i, start, spins = 10000;

	{
	"metadata": {
	"name": "Untitled0"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{

	#!/bin/bash

	# Check out the blog post at:
	#
	# http://www.philipotoole.com/influxdb-and-grafana-howto
	#
	# for full details on how to use this script.

	AWS_EC2_HOSTNAME_URL=http://169.254.169.254/latest/meta-data/public-hostname
	INFLUXDB_DATABASE=test1

	<!DOCTYPE html>
	<html>
	<pre id="page" style="font-family: monospace; white-space: pre-wrap;">
	<h3>A Sinus Activated Multi-Stochastic-Descending/Ascending (MSD/A) Feed Forward Artificial Neural Network (ANN) Computed by GPU via JavaScript and WebGL GLSL (GATO 2014)</h3>This web page attempts to outline an implementation for solving non-linearly-separable (NLS) classification and function approximation problems using a sinus activated feed forward neural network, trained via multi-stochastic-descension/ascension (MSD/A), and evaluated using the GPU via JavaScript and WebGL GLSL source code.

	In order to overcome NLS using MSD/A, a sinus activation function: <b>sin(x)</b>, has been used in place of sigmoid: <b>1 / (1 + exp(-x))</b>, hyper-tangent: <b>htan(x)</b>, and/or averaging: <b>sum / count</b>, activation functions.

	Although ANNs capable of overcoming NLS problems are said to be capable of entering any computationally complete state, actually finding and entering a specific state required to solve a real