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update links to demo in readme

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1 parent 20114ae commit 9e3c1b954571b05965dacbcb3fc3fdedc7526b9a @harthur committed May 12, 2012
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  1. +2 −2 README.md
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@@ -13,7 +13,7 @@ var output = net.run([1, 0]); // [0.987]
```
There's no reason to use a neural network to figure out XOR however (-: so here's a more involved, realistic example:
-[Demo: training a neural network to recognize color contrast](http://harthur.github.com/brain/examples/blackorwhite.html)
+[Demo: training a neural network to recognize color contrast](http://harthur.github.com/brain/)
# Using in node
If you have [node](http://nodejs.org/) you can install with [npm](http://github.com/isaacs/npm):
@@ -27,7 +27,7 @@ Download the latest [brain.js](http://github.com/harthur/brain/downloads). Train
Use `train()` to train the network with an array of training data. The network has to be trained with all the data in bulk in one call to `train()`. The more training patterns, the longer it will take to train, but the better the network will be at classifiying new patterns.
#### Data format
-Each training pattern should have an `input` and an `output`, both of which can be either an array of numbers from `0` to `1` or a hash of numbers from `0` to `1`. For the [color constrast demo](http://harthur.github.com/brain/examples/blackorwhite.html) it looks something like this:
+Each training pattern should have an `input` and an `output`, both of which can be either an array of numbers from `0` to `1` or a hash of numbers from `0` to `1`. For the [color constrast demo](http://harthur.github.com/brain/) it looks something like this:
```javascript
var net = new brain.NeuralNetwork();

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