This repository offers a comprehensive educational initiative for individuals interested in AI, machine learning, and deep learning, using both Apple's MLX and Meta's PyTorch frameworks. It provides detailed books, deep dives into AI papers, and concept explanations, targeting self-learners, Korean students, and newcomers to AI, with the goal of making complex topics accessible through clear explanations and code examples.

How It Works

The project's core approach is to explain AI concepts first using PyTorch, then translate them into MLX, facilitating a deeper understanding by deconstructing material. This dual-framework strategy leverages PyTorch's extensive resources and MLX's emerging capabilities for exploration. The author emphasizes a hands-on learning methodology, advocating for typing code rather than copy-pasting to foster genuine comprehension.

Quick Start & Requirements

• Install dependencies via pip install -r requirements.txt.
• MLX examples require Apple Silicon hardware. PyTorch examples can be run on Intel processors.
• Links to MLX Documentation: https://ml-explore.github.io/mlx/build/html/index.html
• Links to PyTorch Documentation: https://pytorch.org/docs/stable/index.html

Highlighted Details

• Comprehensive educational content structured into three books covering AI fundamentals, MLX specifics, and AI/math concepts.
• "Deep Dives" section offers in-depth analyses of AI models and technologies.
• "Concept Nuggets" provide bite-sized explanations of complex AI topics.
• The repository includes links to related personal projects, such as an illustrated novel on AI consciousness and technical guides.

Maintenance & Community

The author states they are no longer actively adding new content due to "Metal bugs in MacOS" affecting MLX projects. The repository is maintained for reference. The author is focusing on new projects and has provided links to their new repository for additional technical guides.

Licensing & Compatibility

The content is licensed under a restrictive, custom license that prohibits excerpts and requires linking directly to content, providing proper context, and respecting its read-only nature. This is intended to maintain the integrity of ideas and prevent misunderstandings.

Limitations & Caveats

The project is no longer actively maintained with new content due to technical issues with MLX on macOS. The custom license may pose challenges for integration into commercial or collaborative projects due to its restrictions on excerpting and sharing.