Javascript Neural Networks AI (Artificial Intelligence)

Science & Technology

Introduction

Artificial Intelligence (AI) is a broad term encompassing several technologies, one of which is artificial neural networks. This article aims to introduce you to building a simple neural network in JavaScript using the brain.js library.

Why Use Artificial Neural Networks?

Artificial neural networks have the ability to classify data based on recognized patterns. Take Google Photos, for example: when you search for "cat," the network identifies features like fur and whiskers to classify images as cats. In this article, we'll develop a neural network that classifies YouTube videos as either popular or boring based on fake, made-up data.

Setting Up the Project

We'll use Node.js alongside Visual Studio Code as the editor. First, we need to install the brain.js library:

npm install brain.js

Make sure to specify brain.js to avoid installing an older, unmaintained version.

Constructing the Neural Network

Start by importing the brain.js library and creating a neural network object:

const brain = require('brain.js');
const net = new brain.NeuralNetwork();

The default neural network comes with one hidden layer. If needed, more hidden layers can be added.

Preparing Training Data

Neural networks require input and output data to be in arrays with values between 0 and 1. This necessitates data normalization. For our example, we'll normalize views, comments, and likes to fit within this range.

Example Data Set

Below is an arbitrary example of what our input and output data might look like:

const data = [
  ( input: [1, 1, 1], output: { hit: 1 ) },
  ( input: [0.8, 0.75, 0.58], output: { hit: 1 ) },
  ( input: [1, 0, 0], output: { boring: 1 ) },
  ( input: [0.1, 0.2, 0.3], output: { boring: 1 ) }
];

This data represents video metrics normalized to the highest values (100% = 1). The hit: 1 output signifies popular videos, whereas boring: 1 denotes boring videos.

Training the Neural Network

Once the data is prepared, train the network:

net.train(data, (
  log: (error) => console.log(error), // Optionally log the training progress
  logPeriod: 100 // Log progress for every 100 iterations
));

Making Predictions

To classify new video data, use the run method:

const result = net.run([0.65, 0.9, 0.55]);
console.log(result); // Outputs the classification probabilities

Conclusion

This example demonstrates how to implement a simple neural network in JavaScript to classify YouTube video popularity. For more advanced use cases, datasets with clear correlation and possibly different learning mechanisms like linear regression might be needed.

Keyword

  • Artificial Intelligence (AI)
  • Neural Networks
  • JavaScript
  • Node.js
  • brain.js
  • Data Normalization
  • Data Classification
  • Visual Studio Code (VSCode)

FAQ

Q1: What is the brain.js library?

  • brain.js is a JavaScript library for building neural networks, making it easy to implement AI in web and Node.js applications.

Q2: Why normalize data for neural networks?

  • Neural networks require input and output values between 0 and 1. Normalization scales data to fit within this range, ensuring proper functioning.

Q3: How do you increase hidden layers in brain.js?

  • You can increase hidden layers by using new brain.NeuralNetwork(( hiddenLayers: [layer1Neurons, layer2Neurons, ...] )).

Q4: What are some practical use cases of neural networks?

  • Examples include image and speech recognition, fraud detection, medical diagnosis, and recommendation systems.

Q5: Can brain.js be used for complex neural network models?

  • While brain.js is suitable for simple to moderately complex models, more advanced frameworks like TensorFlow.js might be better suited for complex scenarios.