Overview
tinygrad is a compact, educational deep learning framework that demonstrates neural network internals with minimal code, suitable for teaching and lightweight experiments.
Key features
- Minimal implementation: tiny codebase focused on readability and learning.
- Autodiff support: basic backward propagation for small models and examples.
- Lightweight experiments: easy to run on CPU for demos and concept validation.
Use cases
- Teaching and demonstrating deep learning fundamentals.
- Small-scale prototypes and research experiments.
- Reading and contributing to a compact open-source codebase.
Technical details
- Implemented in Python with a simple tensor API and autodiff engine.
- Not intended as a production inference server; optimized for pedagogy.
- Community-maintained and easy to extend for learning purposes.