https://github.com/krasserm/bayesian-machine-learning

Notebooks about Bayesian methods for machine learning
https://github.com/krasserm/bayesian-machine-learning

bayesian-machine-learning bayesian-methods bayesian-optimization gaussian-processes machine-learning variational-autoencoder

Last synced: 4 days ago
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Notebooks about Bayesian methods for machine learning

• Host: GitHub
• URL: https://github.com/krasserm/bayesian-machine-learning
• Owner: krasserm
• License: apache-2.0
• Created: 2018-03-19T14:17:43.000Z (almost 7 years ago)
• Default Branch: dev
• Last Pushed: 2024-03-06T17:26:46.000Z (10 months ago)
• Last Synced: 2025-01-04T04:03:56.614Z (11 days ago)
• Topics: bayesian-machine-learning, bayesian-methods, bayesian-optimization, gaussian-processes, machine-learning, variational-autoencoder
• Language: Jupyter Notebook
• Homepage:
• Size: 27.2 MB
• Stars: 1,838
• Watchers: 77
• Forks: 465
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• StarryDivineSky - krasserm/bayesian-machine-learning - learn、GPy、PyMC3、JAX、Keras、Tensorflow 2 和 Tensorflow Probability 等库的示例实现。 (A01_机器学习教程)

README

        
## Bayesian machine learning notebooks

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4318528.svg)](https://doi.org/10.5281/zenodo.4318528)

This repository is a collection of notebooks about *Bayesian Machine Learning*. The following links display 

some of the notebooks via [nbviewer](https://nbviewer.jupyter.org/) to ensure a proper rendering of formulas.

Dependencies are specified in `requirements.txt` files in subdirectories.

- [Bayesian regression with linear basis function models](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/bayesian-linear-regression/bayesian_linear_regression.ipynb). 

  Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also 

  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/bayesian-linear-regression/bayesian_linear_regression_pymc3.ipynb).

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes.ipynb)

  [Gaussian processes](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes.ipynb?flush_cache=true). 

  Introduction to Gaussian processes for regression. Implementation with plain NumPy/SciPy as well as with scikit-learn and GPy. 

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes_classification.ipynb)

  [Gaussian processes for classification](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes_classification.ipynb). 

  Introduction to Gaussian processes for classification. Implementation with plain NumPy/SciPy as well as with scikit-learn. 

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes_sparse.ipynb)

  [Sparse Gaussian processes](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/gaussian-processes/gaussian_processes_sparse.ipynb). 

  Introduction to sparse Gaussian processes using a variational approach. Example implementation with JAX. 

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/bayesian-optimization/bayesian_optimization.ipynb)

  [Bayesian optimization](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/bayesian-optimization/bayesian_optimization.ipynb). 

  Introduction to Bayesian optimization. Implementation with plain NumPy/SciPy as well as with libraries scikit-optimize 

  and GPyOpt. Hyper-parameter tuning as application example.  

- [Variational inference in Bayesian neural networks](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/bayesian-neural-networks/bayesian_neural_networks.ipynb). 

  Demonstrates how to implement a Bayesian neural network and variational inference of weights. Example implementation 

  with Keras.

- [Reliable uncertainty estimates for neural network predictions](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/noise-contrastive-priors/ncp.ipynb). 

  Uses noise contrastive priors for Bayesian neural networks to get more reliable uncertainty estimates for OOD data.

  Implemented with Tensorflow 2 and Tensorflow Probability.

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/latent-variable-models/latent_variable_models_part_1.ipynb)

  [Latent variable models, part 1: Gaussian mixture models and the EM algorithm](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/latent-variable-models/latent_variable_models_part_1.ipynb).

  Introduction to the expectation maximization (EM) algorithm and its application to Gaussian mixture models. 

  Implementation with plain NumPy/SciPy and scikit-learn. See also 

  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/latent-variable-models/latent_variable_models_part_1_pymc3.ipynb).

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/dev/latent-variable-models/latent_variable_models_part_2.ipynb)

  [Latent variable models, part 2: Stochastic variational inference and variational autoencoders](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/latent-variable-models/latent_variable_models_part_2.ipynb). 

  Introduction to stochastic variational inference with a variational autoencoder as application example. Implementation 

  with Tensorflow 2.x.

- [Deep feature consistent variational autoencoder](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/autoencoder-applications/variational_autoencoder_dfc.ipynb). 

  Describes how a perceptual loss can improve the quality of images generated by a variational autoencoder. Example 

  implementation with Keras.  

- [Conditional generation via Bayesian optimization in latent space](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/dev/autoencoder-applications/variational_autoencoder_opt.ipynb). 

  Describes an approach for conditionally generating outputs with desired properties by doing Bayesian optimization in 

  latent space learned by a variational autoencoder. Example application implemented with Keras and GPyOpt.