However, all is not rosy - deep learning is still theory-heavy and difficult to implement and domain life scientists do not have the time or expertise to delve into these powerful tools. Traditional deep learning is a complicated path that involves evaluating the tools, parameters, datasets, training time and computing power.
Previously, without tight synergies between computer science professionals and ecologists - deep learning work on ecological datasets have proved difficult despite obvious benefits. The few deep learning practitioners are in so much demand from far wealthier giant companies starving the lesser funded ecological world off personnel. This roadmap was popularized by the fast.ai course created by the visionary Jeremy Howard and Rachel Thomas both scientists at the University of San Fransisco and their enthusiastic students who are now implementing these algorithms in other fields and turning whole modes of thinking in traditional industries inside out. This paper demonstrates how to build a simple species classifier that has world-class accuracy. The code can be accessed from the Jupyter Notebook here: https://bit.ly/39woeLt

IMPLEMENTATION

Sea stars are important species in our understanding of marine invertebrate communities.  Intertidal relationships between the sea star Pisaster ochraceus and the mussel Mytilus californicus was actually used to coin the term keystone species \cite{Paine_1966}.  Following that classical study, it would, therefore, be interesting to use sea stars as model species to prototype the classifier AI system.Further, seastars have complicated morphology that might be a challenge even for expert humans -  for this reasons we use these to prototype our AI system. Figure \ref{488749} illustrates our workflow to achieving a that minimum viable product: