Basic Usage

This folder contains notebooks for basic usage of the package, e.g. things like dealing with hyperparameters, parameter constraints and priors, and saving and loading models.

Before checking these out, you may want to check out the introduction to Q-Exponential Process and our simple QEP regression tutorial that details the anatomy of a QPyTorch model.

• Check out our Tutorial on Hyperparameters for information on things like raw versus actual parameters, constraints, priors and more.

• The Saving and Loading Models notebook details how to save and load QPyTorch models on disk.

• The Kernels with Additive or Product Structure notebook describes how to compose kernels additively or multiplicatively, whether for expressivity, sample efficiency, or scalability.

• The Implementing a Custom Kernel notebook details how to write your own custom kernel in QPyTorch.

• The Tutorial on Metrics describes various metrics provided by QPyTorch for assessing the generalization of QEP models.