{"id":25369966,"url":"https://github.com/tschechlovdev/autoencoder_kmeans","last_synced_at":"2025-10-06T13:58:14.024Z","repository":{"id":238811378,"uuid":"797088616","full_name":"tschechlovdev/AutoEncoder_KMeans","owner":"tschechlovdev","description":"Implementation of AutoEncoder in PyTorch for k-Means Clustering","archived":false,"fork":false,"pushed_at":"2024-06-10T07:53:25.000Z","size":24141,"stargazers_count":4,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-24T13:57:34.230Z","etag":null,"topics":["auto-encoder","clustering","deep-learning","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tschechlovdev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2024-05-07T07:12:28.000Z","updated_at":"2025-05-15T15:24:53.000Z","dependencies_parsed_at":"2024-05-08T09:31:19.801Z","dependency_job_id":"1726e841-7610-4365-8b94-147d8abeecd7","html_url":"https://github.com/tschechlovdev/AutoEncoder_KMeans","commit_stats":null,"previous_names":["tschechlovdev/autoencoder_kmeans"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tschechlovdev/AutoEncoder_KMeans","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tschechlovdev%2FAutoEncoder_KMeans","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tschechlovdev%2FAutoEncoder_KMeans/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tschechlovdev%2FAutoEncoder_KMeans/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tschechlovdev%2FAutoEncoder_KMeans/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tschechlovdev","download_url":"https://codeload.github.com/tschechlovdev/AutoEncoder_KMeans/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tschechlovdev%2FAutoEncoder_KMeans/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271285519,"owners_count":24732924,"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","status":"online","status_checked_at":"2025-08-20T02:00:09.606Z","response_time":69,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["auto-encoder","clustering","deep-learning","pytorch"],"created_at":"2025-02-15T01:38:24.856Z","updated_at":"2025-10-06T13:58:08.978Z","avatar_url":"https://github.com/tschechlovdev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Implementation of Auto-Encoders for k-Means Clustering\n\nThis is the implementation of my medium article [\"Deep Auto-Encoders for Clustering: Understanding and Implementing in PyTorch\"](https://medium.com/@tschechd/deep-auto-encoders-for-clustering-understanding-and-implementing-in-pytorch-8cc748a5fa48). \n\nThis repository showcases how to apply Auto-Encoders to learn a lower-dimensional and easier separable presentation of the data.\nThis is especially useful for tasks like clustering.\nTo this end, the well-known MNIST dataset that comprises images of handwritten digits is used.\nThe Auto-Encoder is implemented using PyTorch and scikit-learn is used to apply clustering.\n\nThe notebook shows that the accuracy of k-Means clustering can be improved by more than 20%-points using Auto-Encoders!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftschechlovdev%2Fautoencoder_kmeans","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftschechlovdev%2Fautoencoder_kmeans","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftschechlovdev%2Fautoencoder_kmeans/lists"}