You can use this document to get familiar with GPflow. We've split up the material into four different categories: basics, understanding, advanced needs, and tailored models. We have also provided a flow diagram to guide you to the relevant parts of GPflow for your specific problem.
This section covers the elementary uses of GPflow, and shows you how to use GPflow for your basic datasets with existing models.
In each notebook we go over the data format, model setup, model optimisation, and prediction options.
This section covers the building blocks of GPflow from an implementation perspective, and shows how the different modules interact as a whole.
This section explains the more complex models and features that are available in GPflow.
This section shows how to use GPflow's utilities and codebase to build new probabilistic models. These can be seen as complete examples.
The following notebooks relate to the theory of Gaussian processes and approximations. These are not required reading for using GPflow, but are included for those interested in the theoretical underpinning and technical details.
Carl E Rasmussen and Christopher KI Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
James Hensman, Nicolo Fusi, and Neil D Lawrence. 'Gaussian Processes for Big Data'. Uncertainty in Artificial Intelligence, 2013.
James Hensman, Alexander G de G Matthews, and Zoubin Ghahramani. 'Scalable variational Gaussian process classification'. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015.
Michalis Titsias and Neil D Lawrence. 'Bayesian Gaussian process latent variable model'. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.