Amazing Neural Network Video Demonstration

I recently posted an article featuring a very deep neural network in action (250 layers), see here. Each frame in the video represented one layer, with the signal propagating from one layer to the next. In the last layer, the whole space was classified, in the sense that any new observation was immediately assigned to a particular group. The groups were massively overlapping. The connection structure (the number of connections per neuron) was sparse, allowing for a large number of layers. The purpose was supervised classification.

The example discussed here, though also involving a data animation and a supervised classification problem, illustrates a different aspect of neural networks. This time, there are 5 layers. The purpose, given the picture of a shape, it to classify it (based on a training set) in one of four categories: circle, square, triangle, or unknown. Note that my classification problem also involved four classes.

Interestingly, in my case, the data was standard numerical, tabular observations (synthetic data) turned into images for easy GPU processing. Here, the non-synthetic data consists of actual images, but the video does not feature real images. The roles are reversed. Instead it features the neural network architecture in action, also showing how the signal propagates across the layers until a specific observed shape is assigned to one of the four categories. This offers a very different perspective on how a neural network classifier works: a back-end view of the operations, while my video features a front-end view.

Read full article, including access to source code, here