A collection of neural-network and machine-learning projects, ranging from networks built from scratch in C to notebook-based experiments with PyTorch and scikit-learn.
| Project | Description | Stack |
|---|---|---|
| Simple-Dense-Network | A multi-layer perceptron and K-means, both written from scratch in C with hand-implemented backpropagation, gradient clipping, and a numerically stable softmax. Classifies 2-D points into four non-linearly separable classes. Includes two visualisation notebooks. | C, Jupyter |
| Cache_prefetcher | Smart caching — predicting a program's next memory reference from its address trace, using K-means clustering over the address space and a two-layer LSTM. | Python, PyTorch, scikit-learn |
Each project is self-contained in its own directory, with its own README covering the approach, architecture, and results.
The C project builds with make:
cd Simple-Dense-Network
make
./mlpThe notebook project needs the usual scientific Python stack plus PyTorch:
pip install numpy pandas matplotlib scikit-learn torch jupyterReleased under the Apache License 2.0.