Dependency-Free Neural Network Deployment: From NumPy to Browser

By building a neural network from scratch in NumPy and deploying it to the browser as dependency-free JavaScript, this project reveals what production ML actually entails—far beyond the accuracy metrics emphasized in tutorials. The real value lies in practical techniques like model compression, rigorous deployment verification via parity testing, and ensuring your deployed system behaves identically to your trained model. These habits matter most in real production environments, where resource constraints and verification requirements often trump raw model accuracy.