Prior to 2017, most renditions of neural network models were coded in a batch scripting style. As AI researchers and experienced software engineers became increasingly involved in research and design, we started to see a shift in the coding of models that reflected software engineering principles for reuse and design patterns. A design pattern implies that there is a "best practice" for constructing and coding a model that can be reapplied across a wide range of cases, such as image classification, object detection and tracking, facial recognition, image segmentation, super resolution and style transfer. The introduction of design patterns also helped advance convolutional neural networks (as well as other network architectures) by aiding other researchers in understanding and reproducing a model's architecture. A procedural style for reuse was one of the earliest versions of using design patterns for neural network models.