Move notebooks to new location

- update Github links
- update nbviewer links

Author: krasserm

README.md

@@ -2,44 +2,45 @@
 
 [![DOI](https://zenodo.org/badge/125869131.svg)](https://zenodo.org/badge/latestdoi/125869131)
 
-This repository is a collection of notebooks about *Bayesian Machine Learning*. The following links display 
-the notebooks via [nbviewer](https://nbviewer.jupyter.org/) to ensure a proper rendering of formulas. **Update:** 
-PyMC3 and PyMC4 implementations are now available for some notebooks (more planned).
+This repository is a collection of notebooks related to *Bayesian Machine Learning*. The following links display 
+the notebooks via [nbviewer](https://nbviewer.jupyter.org/) to ensure a proper rendering of formulas.
 
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/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/master/latent_variable_models_part_1.ipynb).
-  Introduction to the expectation maximization (EM) algorithm and its application to Gaussian mixture models. Example
-  implementation with plain NumPy/SciPy and scikit-learn for comparison (see also 
-  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/latent_variable_models_part_1_pymc3.ipynb)).
+- [Variational inference in Bayesian neural networks](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian-neural-networks/bayesian_neural_networks.ipynb). 
+  Demonstrates how to implement a Bayesian neural network and variational inference of network parameters. Example implementation 
+  with Keras (see also 
+  [PyMC4 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian-neural-networks/bayesian_neural_networks_pymc4.ipynb)).
 
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/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/master/latent_variable_models_part_2.ipynb). 
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/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/master/latent-variable-models/latent_variable_models_part_2.ipynb). 
   Introduction to stochastic variational inference with variational autoencoder as application example. Implementation 
   with Tensorflow 2.x.
 
-- [Variational inference in Bayesian neural networks](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian_neural_networks.ipynb). Demonstrates how to 
-  implement and train a Bayesian neural network using a variational inference approach. Example implementation with Keras (see also 
-  [PyMC4 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/wip-bnn-pymc4/bayesian_neural_networks_pymc4.ipynb)).
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/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/master/latent-variable-models/latent_variable_models_part_1.ipynb).
+  Introduction to the expectation maximization (EM) algorithm and its application to Gaussian mixture models. Example
+  implementation with plain NumPy/SciPy and scikit-learn for comparison (see also 
+  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/latent-variable-models/latent_variable_models_part_1_pymc3.ipynb)).
 
-- [Bayesian regression with linear basis function models](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian_linear_regression.ipynb). Introduction to Bayesian
-  linear regression. Implementation from scratch with plain NumPy as well as usage of scikit-learn for comparison (see also 
-  [PyMC4 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian_linear_regression_pymc4.ipynb) and 
-  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/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/master/bayesian-optimization/bayesian_optimization.ipynb)
+  [Bayesian optimization](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian-optimization/bayesian_optimization.ipynb). 
+  Introduction to Bayesian optimization. Example implementations with plain NumPy/SciPy as well as with libraries 
+  scikit-optimize and GPyOpt. Hyper-parameter tuning as application example.  
 
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb)
-  [Gaussian processes](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb). 
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/bayesian-machine-learning/blob/master/gaussian-processes/gaussian_processes.ipynb)
+  [Gaussian processes](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/gaussian-processes/gaussian_processes.ipynb). 
   Introduction to Gaussian processes. Example implementations with plain NumPy/SciPy as well as with libraries 
   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/master/bayesian_optimization.ipynb)
-  [Bayesian optimization](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian_optimization.ipynb). 
-  Introduction to Bayesian optimization. Example implementations with plain NumPy/SciPy as well as with libraries 
-  scikit-optimize and GPyOpt. Hyperparameter tuning as application example.  
+- [Bayesian regression with linear basis function models](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian-linear-regression/bayesian_linear_regression.ipynb). 
+  Introduction to Bayesian linear regression. Implementation from scratch with plain NumPy as well as usage of scikit-learn 
+  for comparison (see also 
+  [PyMC4 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/maste/bayesian-linear-regressionr/bayesian_linear_regression_pymc4.ipynb) and 
+  [PyMC3 implementation](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/bayesian-linear-regression/bayesian_linear_regression_pymc3.ipynb)).
 
-- [Deep feature consistent variational autoencoder](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/variational_autoencoder_dfc.ipynb). 
+- [Deep feature consistent variational autoencoder](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/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/master/variational_autoencoder_opt.ipynb). 
+- [Conditional generation via Bayesian optimization in latent space](https://nbviewer.jupyter.org/github/krasserm/bayesian-machine-learning/blob/master/autoencoder-applications/variational_autoencoder_opt.ipynb). 
   Describes an approach for conditionally generating outputs with desired properties by doing Bayesian optimization in 
-  latent space of variational autoencoders. Example application implemented with Keras and GPyOpt.
+  latent space learned by a variational autoencoder. Example application implemented with Keras and GPyOpt.