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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: about 2 months 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 (over 6 years ago)
- Default Branch: dev
- Last Pushed: 2024-03-06T17:26:46.000Z (7 months ago)
- Last Synced: 2024-07-28T11:33:18.824Z (about 2 months ago)
- Topics: bayesian-machine-learning, bayesian-methods, bayesian-optimization, gaussian-processes, machine-learning, variational-autoencoder
- Language: Jupyter Notebook
- Homepage:
- Size: 27.2 MB
- Stars: 1,787
- Watchers: 76
- Forks: 456
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Bayesian machine learning notebooks
[](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).
- [](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.
- [](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.
- [](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.
- [](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.
- [](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).
- [](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.