Bayesian machine learning notebooks

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

Variational inference for Bayesian neural networks. Demonstrates how to implement and train a Bayesian neural network using a variational inference approach. Example implementation with Keras.
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.
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 over variational inference to stochastic variational inference. Example implementation with Keras.
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.
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.
Topic modeling with PyMC3. An introduction to topic models and their implementation with the probabilistic programming library PyMC3.

rashed091/Bayesian-Deep-Learning

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