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:
├───config : store all configuration files
│
├───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`
│
├───notebooks : contains boilerplate notebook for EDA and quick data understanding/explanations
│
├───output : directory responsible for all output files
│ ├───images : save output images
│ └───savedmodels : save trained model files
│
├───src : source directory
| ├───data_preprocessing : directory containing data preprocessing scripts
│ └───models : directory containing model definations
│
└───utils : utilities directory containing functions and/or submodules