vincent d warmerdam koaning

## scrape_api.py
import requests as rq
import json
import collections
import time

API_KEY  = 'APIKEY'
GROUP_ID = 'GROUPID'

def handle_event(event, event_dict):
    event_dict[ event ] = {}

## index.html
<!DOCTYPE html>
<html>
<head>
	<title>d3.js with leaflet.js</title>

    <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7/leaflet.css"/>
    <script src="http://d3js.org/d3.v3.min.js"></script>
    <script src="http://cdn.leafletjs.com/leaflet-0.7/leaflet.js"></script>
    <script src="http://cdnjs.cloudflare.com/ajax/libs/lodash.js/2.4.1/lodash.js"></script>
    <style>

## index.html
<!DOCTYPE html>
<html>
<head>
	<title>d3.js with leaflet.js</title>

    <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7/leaflet.css"/>
    <script src="http://d3js.org/d3.v3.min.js"></script>
    <script src="http://cdn.leafletjs.com/leaflet-0.7/leaflet.js"></script>
    <script src="http://cdnjs.cloudflare.com/ajax/libs/lodash.js/2.4.1/lodash.js"></script>
    <style>

## server.R
library(shiny)
library(ggplot2)
library(reshape2)
library(randomForest)

df = ChickWeight
n = nrow(df)
perfdf = data.frame(ntree=as.numeric(), mse_train=as.numeric(), mse_test=as.numeric())
count = 0

## gist:28a91f5ac8be1571059a
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## gist:331610c504fac3be01c8
# Neural PCA/Compression in R

In this document I will show a small experiment that shows how a neural network can be used as a pca-algorithm. It should also help explain how neural networks work and why neural networks can be such a powerful machine learning algorithm.

The code will be written in R and needs the following dependencies.

### Packages Used

```{r packages}
library(RSNNS)

## solving_non_linear_problems
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## python_meetup
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## tsp.ipynb

      
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                koaning
                / tsp.ipynb
            
            
              Created
              January 17, 2015 22:57
            
              
                tsp
              
          
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## asdfad
asdfasdfa
	import requests as rq
	import json
	import collections
	import time

	API_KEY = 'APIKEY'
	GROUP_ID = 'GROUPID'

	def handle_event(event, event_dict):
	event_dict[ event ] = {}
	<!DOCTYPE html>
	<html>
	<head>
	<title>d3.js with leaflet.js</title>

	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7/leaflet.css"/>
	<script src="http://d3js.org/d3.v3.min.js"></script>
	<script src="http://cdn.leafletjs.com/leaflet-0.7/leaflet.js"></script>
	<script src="http://cdnjs.cloudflare.com/ajax/libs/lodash.js/2.4.1/lodash.js"></script>
	<style>
	library(shiny)
	library(ggplot2)
	library(reshape2)
	library(randomForest)

	df = ChickWeight
	n = nrow(df)
	perfdf = data.frame(ntree=as.numeric(), mse_train=as.numeric(), mse_test=as.numeric())
	count = 0
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	# Neural PCA/Compression in R

	In this document I will show a small experiment that shows how a neural network can be used as a pca-algorithm. It should also help explain how neural networks work and why neural networks can be such a powerful machine learning algorithm.

	The code will be written in R and needs the following dependencies.

	### Packages Used

	```{r packages}
	library(RSNNS)
	{
	"metadata": {
	"name": "",
	"signature": "sha256:f2df6bbd409a5263ed5975f62791ba9a5f6c84469fdd4212a03e04b0f3bf051d"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [