The template provides a minimal approach for getting started with an AI/ML project, and has hardly any dependencies required. However, the notebooks/BOILERPLATE.ipynb provides popular import and its configurations (like pandas, numpy, scikit-learn and tensorflow). A high level directory overview is as follows:
├───.github : github actions folder
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├───config : store all configuration files
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├───data : responsible for all data handling, or contains raw data
│ └───processed : contains processed data (like combined/normalized dataframes, tables, etc.)
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├───logs : repository to contain log files, can also be saved in `/path/to/directory`
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├───notebooks : contains boilerplate notebook for EDA and quick data understanding/explanations
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├───output : directory responsible for all output files
│ ├───images : save output images
│ └───savedmodels : save trained model files
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├───src : source directory
│ └───models : directory containing model definations
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└───utils : utilities directory containing functions and/or submodules