# Artificial Intelligence

## Brain.js

Brain.js is a JavaScript library that makes it easy to understand Neural Networks because it hides the complexity of the mathematics.

## Building a Neural Network

Building a neural network with Brain.js:

### Example:

// Create a Neural Network
const network = new brain.NeuralNetwork();

// Train the Network with 4 input objects
network.train([
{input:[0,0], output:{zero:1}},
{input:[0,1], output:{one:1}},
{input:[1,0], output:{one:1},
{input:[1,1], output:{zero:1},
]);

// What is the expected output of [1,0]?
result = network.run([1,0]);

// Display the probability for "zero" and "one"
... result["one"] + " " + result["zero"];
Try it Yourself »

### Example Explained:

A Neural Network is created with: `new brain.NeuralNetwork()`

The network is trained with `network.train([examples])`

The examples represent 4 input values with a corresponding output value.

With `network.run([1,0])`, you ask "What is the likely output of [1,0]?"

The answer from the network is:

• one: 93% (close to 1)
• zero: 6% (close to 0)

## How to Predict a Contrast

With CSS, colors can be set by RGB:

### Example

Color RGB
BlackRGB(0,0,0)
YellowRGB(255,255,0)
RedRGB(255,0,0)
WhiteRGB(255,255,255)
Light GrayRGB(192,192,192)
Dark GrayRGB(65,65,65)
Try it Yourself »

The example below demonstrates how to predict the darkness of a color:

### Example:

// Create a Neural Network
const net = new brain.NeuralNetwork();

// Train the Network with 4 input objects
net.train([
// White RGB(255, 255, 255)
{input:[255/255, 255/255, 255/255], output:{light:1}},
// Light grey (192,192,192)
{input:[192/255, 192/255, 192/255], output:{light:1}},
// Darkgrey (64, 64, 64)
{ input:[65/255, 65/255, 65/255], output:{dark:1}},
// Black (0, 0, 0)
{ input:[0, 0, 0], output:{dark:1}},
]);

// What is the expected output of Dark Blue (0, 0, 128)?
let result = net.run([0, 0, 128/255]);

// Display the probability of "dark" and "light"
... result["dark"] + " " + result["light"];
Try it Yourself »

### Example Explained:

A Neural Network is created with: `new brain.NeuralNetwork()`

The network is trained with `network.train([examples])`

The examples represent 4 input values a corresponding output value.

With `network.run([0,0,128/255])`, you ask "What is the likely output of dark blue?"

The answer from the network is:

• Dark: 95%
• Light: 4%

Why not edit the example to test the likely output of yellow or red?