convnetjs

ConvNetJS lets you build and train neural networks—a type of AI model—entirely in your web browser, using JavaScript. Instead of running heavy machine learning code on a server or your computer, everything happens live in the page itself, making it fast to experiment and learn.

A neural network is essentially a mathematical structure that learns patterns from data. Think of it like teaching a system to recognize handwritten digits or classify images. ConvNetJS handles the grunt work: it sets up the network architecture (how many layers, how many neurons per layer), feeds data through it, and adjusts the network's internal "weights" so it gets better at its task over time. The library includes common building blocks like fully connected layers, convolutional layers (especially useful for image processing), and different ways to measure whether the network is doing its job right (like classification or regression).

The demos illustrate what this looks like in practice. You can watch a network learn to recognize handwritten digits from the MNIST dataset, or classify everyday objects from the CIFAR-10 dataset, all running in your browser in real time. There's even a reinforcement learning demo where an AI learns to play a game. For anyone learning machine learning, seeing these examples run live—without installing anything or writing server code—makes the concepts much clearer.

The project was created by Andrej Karpathy (a well-known AI researcher) and is no longer actively maintained, so it's best thought of as an educational tool rather than a production system. It's great for students, researchers, or curious builders who want to understand how neural networks work without the complexity of industrial ML frameworks. You can grab the compiled library and drop it into any web project, or even install it via npm for Node.js.