This repository is a Jupyter Book for the University of Tennessee's GEOG-312 course on geospatial programming. It contains lecture notebooks covering Python fundamentals and geospatial libraries (GeoPandas, Rasterio, Xarray, Leafmap, Geemap, etc.), along with lab exercises. It is primarily an educational resource, not a production application or data pipeline. The content is well-structured with paired Markdown and Jupyter notebook files using jupytext, and includes CI/CD for building the book. While not a reusable software library, the pedagogical patterns and notebook organization are of high quality.
This repository is a Jupyter Book titled 'Learning Machine', providing concise, intuitive explanations of machine learning and deep learning concepts. It is organized into sections covering basics, common tasks, layers, generative models, reinforcement learning, and more. The content is primarily textual with illustrative code snippets using numpy, scipy, and PyTorch. No real datasets are used; all examples are synthetic. The book is designed as a quick-reference handbook for learners, accompanying a university course. The repository is effectively archived by the author, with no planned updates.