This repository showcases several examples for using deep Boltzmann machines (DBMs) employing the Julia package BoltzmannMachines. The examples are implemented as IJulia notebooks, which are best viewed in the Jupyter Notebook Viewer (click the links below). We show examples for:
- training a multimodal DBM on a data set with SNP and gene expression data from patients with acute myeloid leukemia,
- comparing DBMs and conditional generative adversarial networks with respect to conditional sampling on a data set with artificial gene expression and SNP patterns, and
- comparing the dimensionality reduction with DBMs, PCA and t-SNE on an single-cell RNA sequencing data set of neural cells.