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Intro:

  • Introduction
  • GPflow manual
  • GPflow with TensorFlow 2
  • GPflow 2 Upgrade Guide

Examples:

  • Notebooks
    • Basic
      • Bayesian Gaussian process latent variable model (Bayesian GPLVM)
      • Basic (binary) GP classification model
      • Monitoring Optimisation
      • Basic (Gaussian likelihood) GP regression model
    • 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
      • 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
    • Tailor
      • Custom mean functions: metalearning with GPs
      • Mixing TensorFlow models with GPflow
      • Kernel Design
      • Likelihood Design
      • Mixture Density Networks in GPflow
      • Models with observed and latent variables
      • Updating model with new data
    • Theory
      • Comparing FITC approximation to VFE approximation
      • Derivation of SGPR equations
      • Sanity checking when model behaviours should overlap
      • Discussion of the GP marginal likelihood upper bound
      • Derivation of VGP equations
    • Understanding
      • Architecture
      • Manipulating GPflow models
      • Utilities
  • Derivations

API:

  • gpflow
GPflow
  • »
  • Notebooks
  • Edit on GitHub

Notebooks¶

Basic¶

  • Bayesian Gaussian process latent variable model (Bayesian GPLVM)
  • Basic (binary) GP classification model
  • Monitoring Optimisation
  • Basic (Gaussian likelihood) GP regression model

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
  • 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

Tailor¶

  • Custom mean functions: metalearning with GPs
  • Mixing TensorFlow models with GPflow
  • Kernel Design
  • Likelihood Design
  • Mixture Density Networks in GPflow
  • Models with observed and latent variables
  • Updating model with new data

Theory¶

  • Comparing FITC approximation to VFE approximation
  • Derivation of SGPR equations
  • Sanity checking when model behaviours should overlap
  • Discussion of the GP marginal likelihood upper bound
  • Derivation of VGP equations

Understanding¶

  • Architecture
  • Manipulating GPflow models
  • Utilities
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