The template provides a minimal approach for getting started with an AI/ML project, and has hardly any dependencies required. However, the examples/BOILERPLATE.ipynb provides popular import and its configurations (like pandas, numpy, scikit-learn and tensorflow). A high level directory overview is as follows:
├───config : store all configuration files/functions
│
├───data : responsible for all data handling, or contains raw data
│ └───processed : contains processed data (like combined/normalized dataframes, tables, etc.)
│
├───logs : repository to contain log files, can also be saved in `/path/to/directory`
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├───examples : contains boilerplate notebook for EDA and quick data understanding/explanations
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├───output : directory responsible for all output files, useful for code development
│ ├───images : save output images
│ └───savedmodels : save trained model files
│
├───src : source directory
│ ├───agents : define agents for any rnn application
│ ├───engine : provides a suit of machine learning analytic functions
│ └───models : directory containing model definations
│
├───static : other important/useful resources required in the project
│ ├───fonts : store additional fonts, maybe used in documentations
│ ├───images : store explanatory images, maybe used in documentations and/or ipynb/markdowns
│ └───logo : setup a project logo, purely useful for front layer applications can be safely ignored
│
└───utilities : utilities directory containing functions and/or submodules, check readme for more information