ML-Design-Patterns-Cook-Book

ML Design Patterns Cook Book

Description:

This repository provides a collection of Python code examples demonstrating various design patterns applied to machine learning (ML) tasks. These patterns can help improve code readability, maintainability, and flexibility in ML projects.

Contents:

• creational_patterns:
  • factory_method: Examples of using the Factory Method pattern to create different ML models based on configuration.
  • abstract_factory: Examples of using the Abstract Factory pattern to create families of related ML components.
  • singleton: Examples of using the Singleton pattern to ensure only one instance of a class exists, such as for global configuration or resources.
• structural_patterns:
  • adapter: Examples of using the Adapter pattern to adapt existing ML components to different interfaces.
  • composite: Examples of using the Composite pattern to represent hierarchical structures of ML components, such as pipelines or ensemble models.
  • decorator: Examples of using the Decorator pattern to add new responsibilities to ML components dynamically, such as data augmentation or regularization.
• behavioral_patterns:
  • strategy: Examples of using the Strategy pattern to encapsulate different algorithms for a task, such as different optimization algorithms or loss functions.
  • observer: Examples of using the Observer pattern to notify multiple components when a change occurs, such as when a model is updated or a metric changes.
  • iterator: Examples of using the Iterator pattern to iterate over elements of ML collections, such as datasets or model parameters.

Installation:

Clone this repository:

git clone https://github.com/maskedwolf4/ML-Design-Patterns-Cook-Book

Usage:

Explore the code examples in each file to understand how the design patterns are applied to specific ML tasks. You can modify and adapt these examples to fit your own projects.

Contributing:

Contributions are welcome! Feel free to submit pull requests with new examples, improvements, or bug fixes.

License:

This repository is licensed under the MIT License.