Getting Started

This section aims to give you the knowledge necessary to use GPflow on small-to-medium projects, without necessarily going too much into the mathematical and technical details. We do not try to teach the theory behind Gaussian Processes.

• For a brief introduction to the mathematics of Gaussian Processes we recommend this article.

• For a longer text on the theory of Gaussian Processes we recommend the book: Gaussian Processes for Machine Learning.

• If you need a deeper understanding of the technical details or advanced features of GPflow please, see our User Guide.

We will assume you are reasonably familiar with Python, NumPy and maybe TensorFlow, and it is good if you have some previous experience with data processing or machine learning in Python. We do not assume prior knowldege of Gaussian Processes.

• Installation
• Basic Usage with GPR
• Kernels
• Mean Functions
• Parameters and Their Optimisation
• Large Data with SGPR
• Classification, other data distributions, VGP and SVGP
• Monitoring
• Saving and Loading Models