NeuPy is a Tensorflow based python library for prototyping and building neural networks
Computer code collated for use with Artificial Intelligence Engines book by JV Stone
Restricted Boltzmann Machine for collaborative filtering of movies.
Minimum Probability Flow-Boltzmann Machine Learning (MPF-BML) standalone GUI-based application for the inference of maximum entropy distribution parameters
Simple implementation of the standard Boltzmann Machine model with MATLAB
Implementation of Restricted Machine from scratch using PyTorch
Understanding the working of boltzmann machine.
Learn about Recommender Systems: One that predicts binary ratings "Like" or "Not Like", # Boltzmann Machine.
Using Monte-Carlo simulated datasets, a completely transparent Boltzmann Machine trained on 1-D Ising chain data is implemented to predict model couplers in the absence of past coupler values. Methods from machine learning applied to theoretical physics are on display in this work.
The unifying open-source library for Quantum Boltzmann Machines: one Gibbs-state core, six backends (dense, statevector, JAX, tensor-network, quantum-circuit, Pauli propagation), arbitrary QFI metrics, and 13 guided tutorials.
Restricted Boltzmann Machine Creation as Recommendation System for Movie Review
Energy-based (Ising/Boltzmann) learning substrate for parity-3 — can a local, physics-native contrastive rule both compute and learn the couplings? Part of the Physical Learning Substrates portfolio.
This repository contains boltzmann machine, self organizing maps and autoencoder
NeuroBM is a research and educational platform for cognitive modeling using Boltzmann machines. It provides a framework for exploring cognitive dynamics, hypothesis generation, and understanding human-technology interaction patterns. It is only to be used to study how the Human Brain works as technology is all around us in 2025 and onwards.
Boltzmann Machine and Self Organizing Maps are implemented in this repository
Conditional probability for the RTBM.
Add a description, image, and links to the boltzmann-machine topic page so that developers can more easily learn about it.
To associate your repository with the boltzmann-machine topic, visit your repo's landing page and select "manage topics."