Most people, when prompted to imagine a neural network, would visualize that classic model of layered nodes and edges. While the model is certainly helpful in outlining the technical process of deep learning, it offers little insight into what is actually happening. We’re not exposed to any of the inherent structure that the learning process creates. My project addresses this by instead visualizing the neural network as a coordinate transformation that stretches the data into distinct clusters. The learning process thus becomes the process of reshaping the space itself so that classes can be cleanly isolated. Although similar visualizations have been done before, they are typically non-interactive, confined to video demonstrations, or require programming expertise. I am creating a user-friendly model where users can toy with parameters and witness the learning process in real time. My model will also serve as a jumping-off point towards a discussion of the Manifold Hypothesis, and an analysis of the separability between different data sets & how that relates to the structure of their approximate manifolds. The usability of this model is critical for my later research on the influence of hidden layers, where I hope to gain insight into their correlation with the accuracy of Neural Networks.