Gaussian processes are a powerful tool for non-parametric regression. Training is performed by maximizing the likelihood of the data given the model to find the best GP kernel hyperparameters.
Here we compare our TFGP code (based on Tensorflow) to the results using sklearn and Gpy packages.
See the example notebook 'Hyperparameter_training.ipynb'