You could export the results of the models to a file and reference those. With tensorflow you can do this quite easily. I think notebooks are crucial for ML re

You could export the results of the models to a file and reference those. With tensorflow you can do this quite easily. I think notebooks are crucial for ML research, especially in teams. It's how we've been sharing our research with each other(i.e. https://github.com/DanburyAI/SG_DLB_2017 https://github.com/DanburyAI/SG_DLB_2017 ). Wolfram calls these computational essays ( http://blog.stephenwolfram.com/2017/11/what-is-a-computational-essay/ http://blog.stephenwolfram.com/2017/11/what-is-a-computation...). He was actually one of the pioneers in these types of notebooks. Distil.pub has a similar ethos. I think the whole notebook way of doing research is central to reducing research debt ( https://distill.pub/2017/research-debt/ https://distill.pub/2017/research-debt/ ). In the past, it is common to throw away the ladder in mathematical research and write nice lean formal papers. This often produces a type of debt. In a team, the ladder is vitally important. Notebooks help retain the ladder.