Brain.js is one of the AI tools and frameworks we use for client projects.
What is Brain.js?
Brain.js is a powerful JavaScript library that allows developers to build and train neural networks in the browser. With its user-friendly API and extensive documentation, it has become a popular tool for machine learning and artificial intelligence applications. In this article, we’ll take a closer look at how to use Brain.js to train neural networks.
Before we dive into the code, let’s first understand what a neural network is. A neural network is a series of interconnected nodes, or “neurons”, that work together to perform a specific task. Each neuron receives input from other neurons, processes that input, and then sends output to other neurons. By training a neural network, we can teach it to recognize patterns and make predictions based on that data.
To get started with Brain.js, we first need to install it using NPM. Open your terminal and type the following command:
npm install brain.js
Once we have installed Brain.js, we can start building our neural network. The first step is to define the architecture of the network. In Brain.js, we can create a new neural network by using the new brain.NeuralNetwork() constructor. We can then specify the number of input and output nodes, as well as the number of hidden layers and neurons.
const net = new brain.NeuralNetwork({
inputSize: 2,
hiddenLayers: [3],
outputSize: 1
});
In this example, we are creating a neural network with two input nodes, one hidden layer with three neurons, and one output node. The next step is to train the network using a dataset. In Brain.js, we can use the net.train() method to train our neural network.
const trainingData = [ { input: [0, 0], output: [0] },net.train(trainingData);
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] }
];
In this example, we are training our neural network to perform the XOR operation. We have defined a dataset with four examples, where each example consists of an input array and an output array. The input array represents the input values for the XOR operation, and the output array represents the expected output.
Once we have trained our neural network, we can use it to make predictions. In Brain.js, we can use the net.run() method to feed input data into our network and get a prediction.
const output = net.run([0, 1]);
console.log(output); // [ 0.9889301657676697 ]
In this example, we are using our trained neural network to predict the output of the XOR operation for the input values [0, 1]. The output of net.run() is an array that represents the predicted output.
At a high level, here are some ways we use brain.js in our projects:
- Image recognition: Brain.js can be used to build image recognition models that can identify objects within an image. For example, the project “Brain.js Image Classifier” uses a pre-trained neural network to classify images of various objects, such as animals and fruits.
- Sentiment analysis: Brain.js can also be used for sentiment analysis, which involves determining the sentiment of a piece of text (positive, negative, or neutral). The “Sentiment Analysis with Brain.js” project uses a neural network to classify tweets as positive, negative, or neutral.
- Game AI: Brain.js can be used to build game AI models that can learn to play a game by trial and error. For example, the project “AI Flappy Bird” uses a neural network to control a virtual bird that learns to navigate through a set of obstacles.
- Music generation: Brain.js can also be used to generate music by training a neural network on a set of musical patterns. The “Magenta.js” project uses a neural network to generate new melodies and harmonies based on a set of input parameters.
- Chatbots: Brain.js can be used to build chatbots that can understand and respond to natural language inputs. For example, the “Brain.js Chatbot” project uses a neural network to answer questions about the weather.
- Predictive modeling tools: We built a stock market prediction tool that averaged a 54% “correct” rate when it was used within low-volatility periods. This model was blown up pretty quickly when the coronavirus hit, however it would consume historical market data and push out a forward looking prediction for the next 3 days. Since these tools do not exist in a vacuum, you need to factor in the Fed meetings & calendars, random tweets from political figures and other black swan events that impact your models and training.
In conclusion, Brain.js is a powerful tool for building and training neural networks in the browser. By following these simple steps, you can start building your own neural networks and training them to perform a variety of tasks. With its ease of use and extensive documentation, Brain.js is a great choice for developers who are new to machine learning and artificial intelligence.
