{"id":20290138,"url":"https://github.com/karan-malik/restrictedboltzmannmachine","last_synced_at":"2026-04-10T11:01:56.449Z","repository":{"id":112694128,"uuid":"268528291","full_name":"Karan-Malik/RestrictedBoltzmannMachine","owner":"Karan-Malik","description":"Python3 implementation of the Unsupervised Deep Learning Algorithm, Restricted Boltzmann Machine.","archived":false,"fork":false,"pushed_at":"2020-06-01T13:53:07.000Z","size":20349,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-14T08:52:29.101Z","etag":null,"topics":["artificial-intelligence","artificial-neural-networks","boltzmann-machines","deep-learning","deep-learning-algorithms","deep-neural-networks","numpy","numpy-neural-network","python","python3","pytorch","pytorch-implementation","restricted-boltzmann-machine","torch","unsupervised-deep-learning","unsupervised-learning","unsupervised-learning-algorithms","unsupervised-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Karan-Malik.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-01T13:20:52.000Z","updated_at":"2023-10-07T21:01:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"b43d76e9-a379-4139-bda3-5ad19a336a88","html_url":"https://github.com/Karan-Malik/RestrictedBoltzmannMachine","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Karan-Malik%2FRestrictedBoltzmannMachine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Karan-Malik%2FRestrictedBoltzmannMachine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Karan-Malik%2FRestrictedBoltzmannMachine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Karan-Malik%2FRestrictedBoltzmannMachine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Karan-Malik","download_url":"https://codeload.github.com/Karan-Malik/RestrictedBoltzmannMachine/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241787488,"owners_count":20020099,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","artificial-neural-networks","boltzmann-machines","deep-learning","deep-learning-algorithms","deep-neural-networks","numpy","numpy-neural-network","python","python3","pytorch","pytorch-implementation","restricted-boltzmann-machine","torch","unsupervised-deep-learning","unsupervised-learning","unsupervised-learning-algorithms","unsupervised-machine-learning"],"created_at":"2024-11-14T15:06:24.892Z","updated_at":"2025-12-31T01:03:20.713Z","avatar_url":"https://github.com/Karan-Malik.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Restricted Boltzmann Machine\nPython3 implementation of the unsupervised deep learning algorithm Restricted Boltzmann Machine using Pytorch.\n\n## Overview\n\nThe Restricted Boltzmann is a generative and stochastic artificial neural network that is used to learn probability distributions over a set of inputs.\nIt can be used both as an Unsupervised or a Supervised algorithm, depending on the task. It comprises of visible nodes (inputs) and hidden nodes, and uses the contrastive divergence algorithm for training. \n\nIt is used as an unsupervised learning algorithm in this task and is implemented using \n[Pytorch](https://pytorch.org/), an optimized tensor library for deep learning.\n\n#### For theory and working of the Restricted Boltzmann Machine, check out this [research paper](https://christian-igel.github.io/paper/TRBMAI.pdf) by Asja Fischer and Christian Igel.\n\n## Dataset\nThe dataset used was taken from the [Grouplens website](https://grouplens.org/), the Social Computing Research at the University of Minnesota.The data\nhas also been uploaded in the repository under the names ml-1m and ml-100k.\n\nTo download the dataset click [here](https://grouplens.org/datasets/movielens/latest/)\n\n##### Copyright (c) 2020 Karan Malik\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkaran-malik%2Frestrictedboltzmannmachine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkaran-malik%2Frestrictedboltzmannmachine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkaran-malik%2Frestrictedboltzmannmachine/lists"}