Monte Carlo simulations of arbitrary Ising models using RBMs.

Acoustic instrument / sound classification using Fast Fourier Transform, Mel Spectrograms to convert sound files to images for input into Deep Learning models for classification. Models include Restricted Boltzmann Machines, Convolutional Neural Networks, and Inception Architectures

Restricted Boltzmann Machine (classification on MNIST)

RBM for collaborative filtering using metadata

A Deep Learning framework to model the stochastic process of time series.

Use TensorFlow to Generate Music with a Restricted Boltzmann Machine.

Implementation of Restricted Machine from scratch using PyTorch

An Implementation of Restricted Boltzmann Machine with Pytorch

Implementation of a gaussian restricted Blotzmann machine

Compares different Matrix dot product implementations in Ruby

hebbian learning for sequential generation

Machine learning applications

Solved assignments for Deep Learning course @ Faculty of Electrical Engineering and Computing, University of Zagreb

Code for the analysis conducted in the paper "On the Importance of Hidden Bias and Hidden Entropy in Representational Efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines"

Experiments and research of Sum Product Networks (SPFlow and RAT-SPN) and Restricted Boltzmann Machines

Application of Restricted Boltzmann Machines in building a recommender system which predicts binary ratings of movies ( 1 -> Liked, 0 -> Disliked )

Practical works on Bayesian classification, Hidden Markov Models and Restricted Boltzmann Machines

Keras framework for unsupervised learning