Tutorials

In this section you will find tutorials for several algorithms like PCA, ICA, RBMs, … giving you an idea of how you can use the library.

• PCA 2D
• PCA Eigenfaces
• ICA 2D
• ICA on natural images
• FNN on MNIST
• RBM small on MNIST
• RBM big on MNIST
• DBM big on MNIST
• GRBM 2D
• GRBM on natural images
• AE on natural images
• AE on MNIST

The tutorials show how to reproduce results described in the following publications

• Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. Melchior, J., Wang, N., & Wiskott, L.. (2017). PLOS ONE, 12(2), 1–24.
• How to Center Deep Boltzmann Machines. Melchior, J., Fischer, A., & Wiskott, L.. (2016). Journal of Machine Learning Research, 17(99), 1–61.
• Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics Wang, N., Melchior, J., & Wiskott, L.. (2014). (Vol. 1401.5900). arXiv.org e-Print archive.
• How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). arXiv.org e-Print archive.
• An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images. Wang, N., Melchior, J., & Wiskott, L.. (2012). In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292).
• Learning Natural Image Statistics with Gaussian-Binary Restricted Boltzmann Machines. Melchior, J, 29.05.2012. Master’s thesis, Applied Computer Science, Univ. of Bochum, Germany.

For an introduction to Restricted Boltzmann machines especially for Gaussian input variables you can have a look into my master’s theses

A good introduction into several machine learning topics with exercises and video lectures can be found in the here course Material .