Tinygrad

Tinygrad is an open-source, minimalist Python library and deep learning framework designed to provide a simple and understandable implementation of neural network operations. It allows developers, researchers, and hobbyists to experiment with deep learning concepts and automatic differentiation without the complexity of large frameworks.

Features of Tinygrad:

• Implements core neural network operations in a few hundred lines of code.
• Supports automatic differentiation for gradient computation and backpropagation.
• Runs efficiently on CPU, and can optionally leverage GPU acceleration.
• Easy to read and modify, ideal for learning and experimenting with deep learning concepts.
• Compatible with standard Python libraries like NumPy for tensor operations.
• Open-source and actively maintained for educational and research purposes.

To check the installed version of Tinygrad, run these commands in your environment:

$ sudo su
$ cd /opt
$ sudo apt update
$ source tinygrad_env/bin/activate
$ pip show tinygrad

Disclaimer: Tinygrad is primarily designed for educational purposes and small-scale experiments. It is not intended for production-level deep learning workloads. For the latest updates and examples, refer to the official GitHub repository: https://github.com/geohot/tinygrad.