Random fun

Random Fun is a personal collection of experimental scripts and Jupyter notebooks covering machine learning, mathematics, neural networks, and programming ideas. It is not a single application, but a sandbox for compact demonstrations and exploratory work. Included notebooks examine topics such as MicroGrad-style autodiff, mixture density networks, evolution strategies, floating-point behavior, and KNN versus SVMs. Several notebooks explore minimal character-level recurrent neural networks and transformer-related ideas. The repository also contains Rust experiments and lecture material alongside the notebooks. Most of the code is written as self-contained experiments that can be read and modified independently. Its value is primarily educational, showing small implementations of technical concepts without a large framework around them.