Visualizing neural networks in 3d

Artificial neural networks became very popular in recent years, mostly because of their success in tasks of image and speech recognition. While research in this area started more than 60 years ago and many different network architectures were developed during first decades of research, the only architecture that became popular in applications is MLP (multilayer perceptron) -- parametrized multilayer functions trained (optimized) with variations of gradient descent. Later based on MLP approach of training application-specific architectures emerged (such as convolutional and recurrent networks). Probably it is a good idea to understand the behavior of a neural network by visualizing it. While dependencies modelled in machine learning, in particular by neural networks, are multidimensional, we are limited in our visualization abilities to three dimensions.