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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.
Features
- Jupyter-based technical experiments
- Automatic differentiation examples
- Mixture density network notebooks
- Character-level recurrent neural networks
- Transformer and machine-learning experiments
- Rust and lecture-related material
Project Samples
