Examples and Tutorials

This section provides a set of tutorials to help you get started with Minerva and learn how to train models for a variety of machine learning tasks. All tutorials are written as interactive Jupyter notebooks and are organized by topic for ease of navigation

Getting Started Notebooks

These introductory tutorials demonstrate how to use Minerva to train models on two representative tasks using supervised learning and state-of-the-art architectures from the literature:

• Seismic Facies Classification: Explore how to segment seismic images into different facies classes. This is formulated as a semantic segmentation task on 2D image data.

• Human Activity Recognition (HAR): Learn how to classify human activities using time-series data from smartphone sensors (e.g., accelerometers, gyroscopes). This is modeled as a time-series classification problem.

• Experiment API: Understand how to use the experiment API to configure and run experiments, log results, and analyze performance metrics. This is a general-purpose tutorial (constructed over seismic facies classification example) that can be applied to any task.

• Getting Started with Minerva for Seismic Facies Segmentation
• Getting Started with Minerva for Human Activity Recognition
• Minerva Experimental Pipeline