Explainable AI Portfolio

This repository is a lightweight educational project focused on a growing collection of explainable AI resources in computer vision, machine learning, and reinforcement learning. It is designed to support article-ready notebook uploads and sharing in public learning spaces.

Project structure

MyEdProj/
├── README.md
├── .env.example
├── .gitignore
└── 001_computer_vision_XAI/
    └── Explainable_AI_in_Computer_Vision_with_CAM_and_Grad_CAM.ipynb

Included notebook

1. 001_computer_vision_XAI/Explainable_AI_in_Computer_Vision_with_CAM_and_Grad_CAM.ipynb

This notebook covers:

• Standard CAM for GAP-based architectures
• Grad-CAM for more general CNNs
• Visual comparison of activation maps
• Practical model debugging and explainability in computer vision
• 

Local setup

1. Clone the repository.
2. Create a virtual environment:

python -m venv .venv

3. Activate it:

• Windows PowerShell:

.venv\Scripts\Activate.ps1

• macOS/Linux:

source .venv/bin/activate

4. Install dependencies:

pip install torch torchvision opencv-python matplotlib numpy python-dotenv

5. Optionally copy the example environment file:

copy .env.example .env

Support

If you find this work useful, a star would be appreciated. For questions, suggestions, or collaboration opportunities, feel free to open an issue or reach out directly.