Techniques for visualizing the operation of neural networks using samples of training data

This invention describes a way to understand how an artificial intelligence network learns by showing you what happens inside it when it processes training examples. It takes the internal activity data from the network, simplifies it using a technique called dimensionality reduction, and then displays it as a two-dimensional map. This map helps a user visualize how the network has been trained and how it reacts to different data.