INTRODUCTION

Deep learning a branch of machine learning is an artificial intelligence approach which has demonstrated a record-breaking streak in pattern recognition in multiple domains\cite{LeCun_2015}. It has remarkably within a very short time revolutionalized fields from robotics, automotive engineering (self-driving cars), finance, medicine, bioinformatics, games, consumer recommendations and many other fields \cite{Shen_2017,Golden_2017,Min_2016,Heaton_2016,Esteva2019,Esteva2017}.  The hype generated by these streaks has propelled its permeation into everyday computer applications and now increasingly in scientific disciplines including life sciences such as biological and ecological applications\cite{Christin_2019}. Its ease of use, flexibility and record-breaking accuracy has led to an increased interest in its potential in a wide variety of fields\cite{Krizhevsky_2017,Hinton_2012,LeCun_2015}.
In ecology, however, the field is still in its infancy, a literature  review puts both peers and non-peer reviewed papers at 46 as of April 2018 mostly using CNNs and RNNS and non using the recent Deep Reinforcement Learning \cite{Christin_2019}. This is despite its potential to revolutionalize applied ecology in identification and classification of species, behavioural studies, population monitoring and citizen science, ecosystem management and conservation \cite{Christin_2019,Lamba_2019} .
Whereas other machine learning approaches have been used for acoustic classification \cite{Aide_2013}, ecological modelling and studying animal behaviour \cite{Olden_2008,Valletta_2017,Christin_2019}, deep learning approaches have demonstrated the ability to leap frog the bottlenecks and complexity encountered when developing these systems. One of the challenges of machine learning approaches is the need for superior domain knowledge and more than average programming skills which are both expensive and in short supply \cite{LeCun_2015,Christin_2019}.
Ecology is particurarly ripe for the applications of deep learning owing to the explosion of complex ecological datasets over the past few years ranging from genomic to ecosystem-scale data- also known as Big Data. The Big data derived from the increasingly sophisticated automatic monitoring by sensors can no longer be manually processed as it is redundant, tedious, time consuming and sometimes too complex for human beings to comprehend\cite{Weinstein_2017,Norouzzadeh_2018}, hence the need to use more efficient strategies for this. Deep learning is specifically better than other methods in non-linear complex data analysis - data challenges commonly encountered in ecology \cite{Christin_2019}. In fact, all methods for the most recent LifeCLEF contests have been deep learning-based\cite{Joly_2017}. Reviews and proposals for these have been put forward and the field feels right for disruption\cite{Christin_2019,Lamba_2019}. Deep learning has been touted as a contender in solving problems with immediate application ranging from illegal trafficking of wildlife products to large scale automated ecosystem management tools - areas that are expensive and logistically expensive to manage \cite{Cantrell_2017,Christin_2019}
A lot of the bottlenecks of the past are being eliminated with groundbreaking research on transfer learning and data augmentation \cite{Shorten_2019}. This has led to a reduction in the data required to make accurate world-class models. Furthermore, the recent wave in computer hardware innovation for GPU’s and CPU’s has also accelerated by reducing the cost of accessing the processing power required for accurate model development - which is heavy on matrix multiplications. The overall move from the AI winters of the past and a prediction for the “Singularity” also poses interesting opportunities for the future. Life scientists such as ecologists, therefore, need to jump into this bandwagon and take life science to the next level. 
However, all is not rosy - deep learning is still theory-heavy and difficult to implement and domain scientists do not have the time or expertise to delve into these powerful tools. Deep learning is a complicated path that involves evaluating the tools, parameters, datasets, training time and computing power. For this reasons , it still remains siloed in well funded research labs and big technology companies who rarely have the incentive to publicize their processes.
Classical naturalist have identified species for the past two decades laying the foundations of the ecological science that we thrive in today. Therefore to illustrate to non experts how easily they can prototype these previously mysterious tecnhiques this paper takes you step by step on the various stages and offers open source code in form of an annotated Jupyter Notebook that can be used by anybody in the world to produce world class accuracy on whatever supervised species classification they want to carry out. The tutotial is designed in a way that it can be implemented in the lowest resourced environment and unlock great application in species identification in ecology the world over than we can hardly imagine at the moment.
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 data science institute 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 sheds light into the radical shift that has occurred inaccessibility of  AI tools and demonstrates this by outlining how to build a simple species classifier that has world-class accuracy. The code can be accessed in the Jupyter Notebook here: https://bit.ly/2SHz9LYhttps://bit.ly/2SHz9LY

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. Figure \ref{488749} illustrates our workflow to achieving a minimum viable product: