Neural network to classify handwritten digits.
This demo implements a vanilla feed-forward neural network from scratch in order to classify handwritten digits from the MNIST dataset. Users can experiment with different network parameters, including the number of hidden layers, neurons per layer, learning rate, and batch size, to observe their impact on classification accuracy. Training and inference are executed entirely in the browser using Web Workers, ensuring that intensive computations do not block or degrade the responsiveness of the user interface.