Making Neural Networks Transparent and Trustworthy
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Explainable AI - Neural networks can be incredibly accurate, but they’re often considered black boxes. In fields like Earth sciences, we need to understand how a model arrives at its predictions.
This notebook example was developed in AWS SageMaker Studio environment. If you use a local JupyterLab instance, you may need to install relevant Python libraries.
You will need JupyterLab Python environment, either on AWS SageMaker Studio or a local installation.
- JupyterLab Python Environment
- Use real world data instead of the synthetic data
- Add Counterfactuals Framework
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alibilibrary
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See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Makhan Virdi
Project Link: https://github.com/virdi/ai-explained