  Current deep convolutional networks are fixed in their topology. We explore
the possibilites of making the convolutional topology a parameter itself by
combining NeuroEvolution of Augmenting Topologies (NEAT) with Convolutional
Neural Networks (CNNs) and propose such a system using blocks of Residual
Networks (ResNets). We then explain how our suggested system can only be built
once additional optimizations have been made, as genetic algorithms are way
more demanding than training per backpropagation. On the way there we explain
most of those buzzwords and offer a gentle and brief introduction to the most
important modern areas of machine learning
