Machine learning (ML) is a fascinating yet intricate concept, tangled with complex mathematics that intimidates most beginners. This discipline often seems impregnable due to its association with intense mathematical equations and seemingly arcane terminology.
With the advent of Brain.js, one can navigate machine learning much more effortless, abstaining from intimidating math and allowing beginners a smooth entryway into this intriguing world. This article demonstrates how Brain.js facilitates easy understanding of neural networks, in the context of a practical example.
Understanding the fundamentals
Imagine a neural network as the human brain, a network of interconnected nodes divided into layers. These layers include input, hidden, and output layers, and data moving unidirectionally from input to output.
The neural network consumes a volume of labeled data, learns the patterns and associations, makes predictions, and then mirrors desirable outputs. Astonishingly, the accuracy and speed of neural networks outshine the human brain!
Shifting gears to Brain.js
With Brain.js, it becomes feasible to build a neural network, train it with a data set, and gradually get your model to predict based on the learned data.
All Hands on deck
First, install Brain.js with npm using the command
npm install brain.js. Assuming…