Code Tutorials & Jupyter Notebooks

This folder holds fully-documented, interactive Jupyter Notebooks detailing end-to-end processing of satellite imagery and hyperspectral data arrays.

These notebooks are converted dynamically to webpage documents with all code output, figures, and maps rendered inline.

🚀 Active Notebook walkthroughs

Spatio-Temporal & Computer Vision

NASA PACE Ocean Color Analysis: Detailed atmospheric correction and spectral analysis workflows targeting NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) hyperspectral sensor data.
Sentinel-2 Change Detection: Preprocessing, spectral indexes calculations, classification pipelines, and change-detection maps using free Sentinel-2 imagery.

Natural Language Processing (NLP) & GenAI

In addition to physical modeling and computer vision, I apply deep learning to NLP problems to enable project discovery, metadata tagging, and literature classification: * Named Entity Recognition (NER) Foundations: Processing text with modern Transformer pipelines to extract domain entities, organizations, and spatial markers. * Topic Modeling & Semantic Structuring: Organizing unstructured scholarly logs, abstracts, or communications into coherent latent thematic models. * Sentiment Analysis & Zero-Shot Classification: Fusing pre-trained language models to tag and classify documents or query inputs on arbitrary criteria without prompt training.

Running Notebooks Locally

All tutorials are run in isolated Python kernels handled through the pixi environment manager. To clone and host these notebooks on your machine:

Clone this repository:

git clone https://github.com/bidhya/githubio.git
cd githubio

Install dependencies and switch on the environment using pixi:
```
pixi run jupyter notebook
```