This repository is a collection of notebooks covering various topics of Bayesian methods for machine learning.
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Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation from scratch with plain NumPy as well as usage of scikit-learn for comparison.
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Gaussian processes. Introduction to Gaussian processes. Example implementations with plain NumPy/SciPy as well as with libraries scikit-learn and GPy.
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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.
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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.
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Conditional generation via Bayesian optimization in latent space. Describes an approach for conditionally generating outputs with desired properties by doing Bayesian optimization in latent space of variational auto-encoders. Example application implemented with Keras and GPyOpt.
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Deep feature consistent variational auto-encoder. Describes how a perceptual loss can improve the quality of images generated by a variational auto-encoder. Example implementation with Keras.
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Topic modeling with PyMC3. An introduction to topic models and their implementation with the probabilistic programming library PyMC3.
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