Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

Notes for derivatives for machine learning in Jupyter Notebook

Summary notebooks using derivative gaussian processes with tinygp. We implement a 2D derivative gaussian process and successfully use derivatives to regularize SVI fits with a gaussian process model..

I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition