https://github.com/dharma-acha/explanability_in_deepneuralnetworks
Our project aims to enhance the transparency and trustworthiness of the VGG model in critical fields like healthcare imaging and self-driving cars. By integrating explainability methods into the VGG model for image classification, we will clarify its decision-making process.
https://github.com/dharma-acha/explanability_in_deepneuralnetworks
colab-notebook matplotlib numpy pandas scikit-learn seaborn
Last synced: 3 months ago
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Our project aims to enhance the transparency and trustworthiness of the VGG model in critical fields like healthcare imaging and self-driving cars. By integrating explainability methods into the VGG model for image classification, we will clarify its decision-making process.
- Host: GitHub
- URL: https://github.com/dharma-acha/explanability_in_deepneuralnetworks
- Owner: dharma-acha
- Created: 2024-05-30T05:48:37.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-06T05:03:58.000Z (about 2 years ago)
- Last Synced: 2026-04-30T20:32:57.781Z (3 months ago)
- Topics: colab-notebook, matplotlib, numpy, pandas, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 10.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md