Welcome to the Maudlin Documentation repository! This documentation provides comprehensive information about the Maudlin framework, its features, and how to use it effectively.
- Getting Started - A guide to help new users begin working with Maudlin.
- Workflow - An overview of Maudlin's workflow, detailing the processes involved.
- Configuration - Information on configuring Maudlin for various use cases.
- Featurizers - Documentation on feature engineering functions used in Maudlin.
- Training - Instructions on training models within the Maudlin framework.
- Prediction - Guidelines for making predictions using trained models.
- Customization - Details on customizing Maudlin to fit specific needs.
- History - Information on tracking and managing experiment history.
Maudlin is a framework designed for rapid experimentation with neural networks. It streamlines the process of model creation, configuration, training, and evaluation. Key features include:
- YAML-based configuration for defining models and workflows.
- Support for feature engineering and preprocessing.
- Flexible training and prediction pipelines.
- Tools for visualizing and interpreting results.
Follow the Getting Started guide to set up Maudlin and create your first experiment.
Contributions are welcome! Please refer to our contributing guide for instructions on how to contribute to this project.
This project is licensed under the MIT License. See the LICENSE file for more details.
For further details on specific features, please explore the links in the table of contents above.