The repository is currently a work in progress. Here is a tentative list of the demo notebooks that will be included:
1. "An invitation to sketched learning", which shows on a very-large-scale dataset how compressive learning saves computational resources
2. "Building intuition for sketched learning", a notebook to play around with to understand why sketched learning works (vizualization of the cost function, etc.)
3. "In-dept sketched learning", where we discuss practical aspects such as how to chose the "sketch scale parameter"
4. "Quantization aspects", where we show how sketch contribution can be quantized at essentially no cost
5. "Privacy-preserving sketched learning", where we demonstrate how our sketches can be used to learn with differential privacy guarantees
