Empowering Capacitive Devices: Harnessing Transfer Learning for Enhanced Data-Driven Optimization

Folders

1. cdi: contains the python functions for training models. You'll find scripts, modules, and packages here that are used to implement the algorithms and methods involved in the work.

2. data: store any necessary data files required for training models. This could include datasets, preprocessed data, or any other data-related resources.

3. notebooks: contains Jupyter notebooks that showcase the step-by-step process of using functions in src. These notebooks are meant to provide interactive examples, tutorials, and documentation for using the code in the src folder.

4. model: save trained models, model weights and other pickle files.

License

This project is licensed under the MIT License.

Feel free to contribute, open issues, or submit pull requests if you have any improvements or ideas to share. Happy reading!