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.

This demo implements a visualizer to compare different pathfinding algorithms with mutliple heuristic options. Users can interactively place start and end nodes, draw obstacles, and observe how each algorithm explores the grid. The visualization highlights the nodes visited, the final shortest path, and the performance differences between algorithms in terms of speed and efficiency. This tool is useful for understanding the mechanics and trade-offs of each algorithm in real time.