Notebooks related to Bayesian methods for machine learning
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README.md

Bayesian machine learning notebooks

This repository is a collection of notebooks covering various topics of Bayesian methods for machine learning.

Basics

  • Gaussian processes. Introduction to Gaussian processes. Example implementations with plain NumPy/SciPy as well as with libraries scikit-learn and GPy.

  • Bayesian optimization. Introduction to Bayesian optimization. Example implementations with plain NumPy/SciPy as well as with libraries scikit-optimize and GPyOpt. Hyperparameter tuning as application example.

  • Variational auto-encoder. A guide to variational auto-encoders described as a journey from expectation maximization (EM) algorithm via variational inference to stochastic variational inference. Example implementation with Keras.

  • Topic modeling with PyMC3. An introduction to topic models and their implementation with the probabilistic programming framework PyMC3.

  • ...

Applications