This is a short series on Gaussian Processes I've been working on for a while. It's a 3 part series:

1. Part 1 introduces the concept of Gaussian processes with the help of a simple Python implementation: https://peterroelants.github.io/posts/gaussian-process-tutor...

2. Part 2 illustrates how to practically fit kernel hyperparameters to data with the help of TensorFlow probability: https://peterroelants.github.io/posts/gaussian-process-kerne...

3. Part 3 goes more in-depth in the different kernels fitted in part 2: https://peterroelants.github.io/posts/gaussian-process-kerne...