Deep learning is a powerful method using neural networks to learn functional representations that relate variables of interest. This paper examines the manner of representation of those variables by neural networks and of neural networks by humans. In the first section, we examine causal relations among variables with CausalGAN. The following section will explore a theoretical connection between neural networks and support vector machines (SVMs) representing neural network functions through a sample compression scheme. The third section reparamaterizes neural networks using Min and Max combinations of linear functions and examines the connection with generalization and interpretation. The final section explores applications of this method to ECG model interpretation.