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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Machine Learning - Javascript</title>
<meta name="description" content="Machine learning in Javascript">
<meta name="author" content="Piero Savastano">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="icon" type="image/x-icon" href="favicon.ico">
<link rel="stylesheet" href="css/reveal.css">
<link rel="stylesheet" href="css/theme/moon.css" id="theme">
<link rel="stylesheet" href="css/pieroit.css">
<!-- Code syntax highlighting -->
<link rel="stylesheet" href="lib/css/zenburn.css">
<!-- Printing and PDF exports -->
<script>
var link = document.createElement( 'link' );
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = window.location.search.match( /print-pdf/gi ) ? 'css/print/pdf.css' : 'css/print/paper.css';
document.getElementsByTagName( 'head' )[0].appendChild( link );
</script>
<!--[if lt IE 9]>
<script src="lib/js/html5shiv.js"></script>
<![endif]-->
</head>
<body>
<div class="reveal">
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<!-- Intro -->
<section>
<h1><a href="index.html">Machine Learning</a></h1>
<h2>Javascript</h2>
<ul>
<li>Overview of open source libraries</li>
<li>Recommend textual content</li>
</ul>
</section>
<!-- Panorama -->
<section>
<h2>Open source libs</h2>
<ul>
<li>Natural</li>
<li>ConvNetJS</li>
<li>(statistics libs)</li>
<li>???</li>
</ul>
</section>
<section>
<section>
<a href="https://github.com/NaturalNode/natural">
<img width="100%" src="img/naturalJS.png" />
</a>
</section>
<section>
<pre class="stretch"><code class="hljs" data-trim>
var natural = require('natural'),
classifier = new natural.BayesClassifier();
classifier.addDocument('my unit-tests failed.', 'software');
classifier.addDocument('tried the program, but it was buggy.', 'software');
classifier.addDocument('the drive has a 2TB capacity.', 'hardware');
classifier.addDocument('i need a new power supply.', 'hardware');
classifier.train();
classifier.save('classifier.json', function(err, classifier) {
// the classifier is saved to the classifier.json file!
natural.BayesClassifier.load('classifier.json', null, function(err, classifier) {
console.log(classifier.classify('did you buy a new drive?'));
});
});
</code></pre>
</section>
</section>
<section>
<section>
<a href="http://cs.stanford.edu/people/karpathy/convnetjs/">
<img width="100%" src="img/convnetjs.png" />
</a>
</section>
<section>
<div>"The Deep Learning Conspiracy"</div>
<a href="http://www.kdnuggets.com/wp-content/uploads/photo.jpg">
<img width="100%" src="img/deep_learning_conspiracy.jpg" />
</a>
</section>
</section>
<!-- Statistics libs -->
<section>
<a href="http://simplestatistics.org/">
<img width="100%" src="img/simplestatisticsjs.png" />
</a>
</section>
<section>
<a href="http://jstat.github.io/">
<img width="100%" src="img/jstat.png" />
</a>
</section>
<section>
<h2>Takeaway</h2>
<ul>
<li>Algorithm vs. Model</li>
<li>Javascript is good to run the Model</li>
</ul>
</section>
<!-- Example -->
<section data-background="img/graph_viz.png" class="white">
<h2>Let's write a model</h2>
<ul>
<li>Data gathering and cleaning</li>
<li>Algorithm choice</li>
<li>Model training</li>
<li>Recommend</li>
</ul>
</section>
<section>
<h2>Get data</h2>
<div>Italian food</div>
<pre><code class="hljs" data-trim>
SELECT DISTINCT ?id ?label ?content WHERE {
?id a dbo:Food .
?id dbo:origin dbr:Italy .
?id dbo:abstract ?content .
?id rdfs:label ?label .
FILTER(LANG(?label) = "en")
FILTER(LANG(?content) = "en")
} LIMIT 10000
</code></pre>
<a href="http://dbpedia.org/sparql">
Run on dbPedia
</a>
</section>
<!-- Measuring similarity between texts -->
<section>
<section>
<h2>Algorithm</h2>
<div>Jaccard/Tanimoto index</div>
<img src="img/tanimoto.png" />
</section>
<section>
<h2>Algorithm</h2>
<div>Jaccard/Tanimoto index</div>
<pre><code class="hljs" data-trim>
function similarity( bagA, bagB ) {
var nIntersection = _.intersection( bagA, bagB).length;
var nUnion = _.union( bagA, bagB ).length;
return nIntersection / nUnion;
};
</code></pre>
</section>
</section>
<!-- Build similarity graph -->
<section>
<h2>Similarity graph</h2>
<span>
<img height="200px" src="img/similarity_graph.jpg" />
</span>
<pre><code class="hljs" data-trim contenteditable>
var similarityGraph = {
"A":{
"B": 0.1,
"C": 0.8,
},
"B":{
"A": 0.1,
"C": 0.5,
},
"C":{
"A": 0.8,
"B": 0.5,
}
};
</code></pre>
</section>
<!-- Demo -->
<section>
<img width="800px" src="img/demo.jpg" />
<div>
<a href="https://github.com/pieroit/machine-learning-open-course/tree/gh-pages/nodejs-example">Show me the code!</a>
</div>
</section>
</div>
<script src="lib/js/head.min.js"></script>
<script src="js/reveal.js"></script>
<script>
// Full list of configuration options available at:
// https://github.com/hakimel/reveal.js#configuration
Reveal.initialize({
controls: true,
progress: true,
history: true,
center: true,
transition: 'slide', // none/fade/slide/convex/concave/zoom
// Optional reveal.js plugins
dependencies: [
{ src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } },
{ src: 'plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
{ src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
{ src: 'plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } },
{ src: 'plugin/zoom-js/zoom.js', async: true },
{ src: 'plugin/notes/notes.js', async: true }
]
});
</script>
</body>
</html>