# Notebooks¶

## Basic¶

## Advanced¶

- More details on models with many observation points
- Change points
- Convolutional Gaussian Processes
- A simple demonstration of coregionalization
- Stochastic Variational Inference for scalability with SVGP
- Heteroskedastic Likelihood and Multi-Latent GP
- Data Generation
- Plot Data
- Build Model
- Inducing Points
- Build Optimizers (NatGrad + Adam)
- Run Optimization Loop
- Manipulating kernels
- MCMC (Markov Chain Monte Carlo)
- Multiclass classification
- Multi-output Gaussian processes in GPflow
- Natural gradients
- Optimizers
- Ordinal regression
- Variational Fourier Features in the GPflow framework
- Gaussian process regression with varying output noise