Conceptual diagram of the workflow for developing a species classifier model - made with illustrations from Lucidchart.com and Amazon Web Services icons

Libraries and Tools

Table 1: Fancy table with the different libraries

I. DATA PHASE

Data Collection

Table 2: Table with where to get data

Data Preparation

Data Organization - Training, Validation and Test set

II. DEEP LEARNING PHASE

model creation

Figure 2: show confusion matrix - look at cancer paper by thrun published in nature
Table 3: show table of results

model tuning

model saving

model inference - development

III. DEPLOYMENT PHASE

model inference - production

model inference - deployment

Figure 3: show final product

Conclusion

Although there have been many papers prototyping interesting apllications of artificial intelligence, very few outline beginner friendly - non domain approaches to classify species. This paper illustrated the tools, steps and resources required to achieve this feat with little domain knowledge of the subject. Like, the advent of electricity in 19XX(quote edison) - it is the application of the tools that will move humanity forward. This paper gives ecologists working from the most advanced labs in the world to the least resourced labs power to prototype world class classifiers for their specific subjects.

Acknowledgements (optional)

I would like to thank my advisor Dr. Melissa Pespeni and the QUEST fellowship under grant from NSF bla bla
A brief statement acknowledging collaborators and research assistants who do not meet the criteria for authorship described above, or acknowledging funding sources, providing relevant permit numbers (including institutional animal use permits), or giving recognition to nature reserves or other organizations that made the work possible.

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