Artificial Intelligence and Machine Learning: Revolutionizing Lives
Artificial Intelligence and Machine Learning are revolutionizing various aspects of our lives. One key element of these technologies is Neural Networks. Neural Networks mimic the human brain’s function to solve complex problems. However, understanding and applying Neural Networks can be a daunting task due to the intricate mathematics involved. But, JavaScript library Brain.js simplifies this process.
Brain.js is a powerful tool that makes creating and understanding Neural Networks a breeze. It hides the complexity of the mathematics, making it accessible to a broader audience. This article will guide you through building a Neural Network with Brain.js and even applying it to fun examples, like predicting color contrasts.
Building a Neural Network with Brain.js
Creating a Neural Network with Brain.js is as simple as declaring a new instance of the brain.NeuralNetwork class. Then, using the train function, you can train the network with examples. Each example is an object with an input array and a corresponding output. Once the network is trained, you can use the run function to ask the network for an output based on a given input.
For instance, consider a binary input system. You train the network with four different input-output pairs. When you run the network with [1,0] as input, the output probabilities for ‘zero’ and ‘one’ are displayed. You’ll find the probability for ‘one’ is close to 93% and for ‘zero’ is close to 6%.
Predicting Color Contrasts with Neural Networks
But Brain.js isn’t limited to binary systems. It can be used for more complex and fascinating applications. One such application is predicting the darkness or lightness of a color.
Colors in CSS are set using RGB values. You can train a Neural Network to associate certain RGB values with ‘dark’ or ‘light’. For example, you can train the network with the RGB values for white, light gray, dark gray, and black, each associated with either ‘light’ or ‘dark’.
When you run the network with the RGB values for dark blue, the network predicts the color’s darkness or lightness. In this case, the network predicts a 95% probability for ‘dark’ and a 4% probability for ‘light’.
You can even edit the example to test the likely output of yellow or red, or any other color that you are interested in predicting.
As you can see, Brain.js is an incredibly powerful tool for understanding and applying Neural Networks. Whether you’re a seasoned developer or a beginner, Brain.js can help you harness the power of Neural Networks to solve complex problems and make your applications smarter.
For more information on how non-techies in Bengaluru are embracing the coding revolution with AI, check out this article here. Don’t forget to subscribe to aitechtrend.com for the latest updates on AI and technology trends.