25hency/Nutritional-Deficiency-Detection-using-Deep-Learning

AI Summary: Create at least three Jupyter Notebook tutorials demonstrating key workflows (data preparation, model training, evaluation) of the deep learning project for detecting nutritional deficiencies from skin images. These tutorials should include instructions for integrating custom datasets, a troubleshooting section addressing common issues, and step-by-step explanations.

AI Summary: The task is to add comprehensive API documentation to the existing deep learning project. This includes writing docstrings for all public functions and classes, generating a documentation site (e.g., using Sphinx), and providing usage examples for major components. Type hints should also be added throughout the codebase.

AI Summary: Develop visualization tools for the deep learning model's predictions, including heatmaps for image inputs, an interactive dashboard for exploring predictions, and a comparison view for different models. This involves using techniques like Grad-CAM for heatmaps and potentially Streamlit or Dash for the interactive dashboard.

AI Summary: Improve the algorithm that links NHANES health data to dermatological images, add statistical validation methods to assess the quality of the mapping, and create interactive visualizations to examine the mappings. This involves enhancing the existing `data_mapper.py` and potentially adding new visualization components.