REDDITARUN / Meta_ML

Meta-Learning Codebooks

This repository contains implementation code for two significant meta-learning models: Siamese Networks for face recognition and Prototypical Networks for classification tasks. These models represent cutting-edge approaches in machine learning that facilitate the process of learning from fewer examples with high efficiency.

Models in this Repository

Siamese Networks for Face Recognition

Siamese Networks are uniquely effective for tasks that involve verifying whether two inputs are similar or dissimilar. They are especially well-suited for face recognition tasks, leveraging twin networks to compare features with remarkable precision.

Notebook: Meta_Learning_1_Siamese_Network_Face_Recognition.ipynb

For a detailed tutorial on Siamese Networks, check out the following article:

• Beyond Deep Learning: How Meta-Learning Unlocks Powerful AI - Link

Prototypical Networks for Classification

Prototypical Networks excel in classifying data into categories based on few examples by learning a metric space in which classification can be performed by computing distances to prototype representations of each class.

Notebook: Meta_Learning_2_Prototypical_Network_Face_Classification.ipynb

For more insights into Prototypical Networks, visit this article:

• Beyond Deep Learning: Meta-Learning for Efficient AI - Link

Repository Structure

• Data.zip: Contains the datasets used in the notebooks.
• Meta_Learning_1_Siamese_Network_Face_Recognition.ipynb: Jupyter notebook for the Siamese Networks.
• Meta_Learning_2_Prototypical_Network_Face_Classification.ipynb: Jupyter notebook for the Prototypical Networks.
• LICENSE: License file for the project.
• README.md: This README file.

References

• Ravichandiran, Sudharsan. Hands-On Meta Learning with Python. 2018.
• Hands-On Meta Learning With Python - GitHub Repository
• Jason Brownlee. "Meta-Learning in Machine Learning." Machine Learning Mastery
• Cnielly. "Prototypical Networks on the Omniglot Dataset." GitHub Repository

Getting Started

To get started with these notebooks:

1. Clone this repository.
2. Unzip Data.zip and place the data in the appropriate directories as specified in the notebooks.
3. Install necessary dependencies as listed in each notebook.
4. Run the notebooks to train the models and explore the fascinating world of meta-learning.

Contributors

• REDDITARUN - Initial work and updates.

Feel free to contribute to this project by submitting a pull request or opening an issue!