Machine Learning

for JavaScript Hackers

Machine Learning

Machine Learning

Machine Learning

Solves tasks that people are good at, but traditional computation is bad at.

Examples

traditional computing: Hand-chosen conditionals and params.
ML algorithms: Rules are learned from data

Training

Let's try something

The problem

Should I bother?...
Does it have any cat pics?
???

Cat Detection

Breaking it down

does this image contain a cat?

Breaking it down

is this subsection a cat head?

We need a classifier

neural networks

      
        function runNetwork(input) {
          var net = {"layers":[{"0":{},"1":{}},{"0":{"bias":5.1244897557632765,"weights":{"0":-3.591317000303657,"1":-3.594502936141513}},"1":{"bias":1.4480619514263766,"weights":{"0":-5.021099423700753,"1":-5.055736046304716}},"2":{"bias":0.655017127607016,"weights":{"0":-3.9842614825641096,"1":-4.020357237374914}}},{"0":{"bias":-3.093322979654723,"weights":{"0":7.328941033927063,"1":-5.699647431673055,"2":-3.879799253666414}}}]};

          for (var i = 1; i < net.layers.length; i++) {
            var layer = net.layers[i];
            var output = {};

            for (var id in layer) {
              var node = layer[id];

              var sum = node.bias;
              for (var iid in node.weights) {
                sum += node.weights[iid] * input[iid];
              }
              output[id] = (1 / (1 + Math.exp(-sum)));
            }
            input = output;
          }
          return output;
        }
Not going to go into how it works, but these floating point values are trained. Optimization problem, try to find floating point values that minimize the error in the training set.

input and output

    
      net.train([
        { input: [0.7, 0.1, 0.3], output: [1] },
        { input: [1.0, 0.8, 0.7], output: [0] },
        { input: [0.5, 0.6, 0.7], output: [0] }
      ]);

      var output = net.run([0.5, 0.5, 0.6]);   // [0.001]

What is the input?

Pixels
too much variation
Edges
leaves shape information
want to make it easy on the network, giving it data it can get a better handle on, data that doesn't vary too much

Histogram of Oriented Gradients

Collection

Training

    
    var hog = require("hog-descriptor");
    var brain = require("brain");

    var data = pics.map(function(pic) {
      return {
        input: hog.extractHOG(pic.canvas),
        expected: [pic.cat ? 1 : 0]
      }
    })

    var net = new brain.NeuralNetwork();

    net.train(data);

Is this subsection a cat head?

    
    function isCat(canvas) {
      var features = hog.extractHOG(canvas);

      var prob = net.run(features);

      return prob > 0.9;
    }

Does this image contain a cat?

Demo

http://harthur.github.com/kittydar

JavaScript?

Not As Fun

If you do

Libraries

Some Apps