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CS224n with MLX: Implementing NLP Models on Apple Silicon

Overview

This repository contains implementations of key concepts and models from the Stanford CS224n: Natural Language Processing with Deep Learning course, using Apple's MLX deep learning library. The goal is to leverage the optimized performance of MLX on Apple Silicon to build and train state-of-the-art NLP models.

Table of Contents

• word vectors
• recurrent neural networks
• transformers

Contributing

I welcome contributions to this project! If you have suggestions for improvements or new features, please open an issue or submit a pull request. For major changes, please discuss them in an issue first to ensure they align with the project's goals.

Acknowledgments

• Stanford CS224n: This project is inspired by the Stanford CS224n course on Natural Language Processing with Deep Learning.
• Apple MLX: Special thanks to Apple's machine learning research team for developing the MLX library.