Softmax and K-means Clustering

Sibylle explains how Softmax compares embedded vectors to class defining vectors, resulting in cone-shaped representations of classes in the embedded space. The larger the embedded input vector is away from the origin, the higher the confidence. This chapter dives into the connection between Softmax and K-means clustering.