{"id":21026650,"url":"https://github.com/jeremiegince/learning_svm","last_synced_at":"2025-10-30T04:43:57.887Z","repository":{"id":193909714,"uuid":"688717307","full_name":"JeremieGince/Learning_SVM","owner":"JeremieGince","description":"Support vector machines (SVM) tutorial with classical and quantum kernels.","archived":false,"fork":false,"pushed_at":"2023-09-25T16:30:28.000Z","size":1790,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-20T14:34:57.678Z","etag":null,"topics":["kernel","quantum-computing","svm","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JeremieGince.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":"2023-09-08T00:35:14.000Z","updated_at":"2023-09-10T17:35:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"4a4014c2-8565-4458-a633-41c1383f57c4","html_url":"https://github.com/JeremieGince/Learning_SVM","commit_stats":null,"previous_names":["jeremiegince/learning_svm"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JeremieGince%2FLearning_SVM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JeremieGince%2FLearning_SVM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JeremieGince%2FLearning_SVM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JeremieGince%2FLearning_SVM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JeremieGince","download_url":"https://codeload.github.com/JeremieGince/Learning_SVM/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243463491,"owners_count":20295118,"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":["kernel","quantum-computing","svm","tutorial"],"created_at":"2024-11-19T11:45:41.881Z","updated_at":"2025-10-30T04:43:57.804Z","avatar_url":"https://github.com/JeremieGince.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Support vector machines (SVM)\n## Introduction\nSupport vector machines (SVM) is a supervised machine learning algorithm which can be used for both classification or \nregression challenges. However,  it is mostly used in classification problems.\n\nThis repository contains the implementation of SVM algorithm from scratch in python and also using sklearn library.\n## Table of contents\n* [Introduction](#introduction)\n* [Jupyter notebook](https://github.com/JeremieGince/Learning_SVM/blob/main/notebook.ipynb)\n* [Google Colab](https://colab.research.google.com/github/JeremieGince/Learning_SVM/blob/main/notebook.ipynb)\n* [Scripts](#Scripts)\n* [Requirements](#requirements)\n* [Slides](Prez_SVM.pdf)\n\n## Scripts\n* In the [main.py](main.py) file, you can find the training, testing and comparison of the SVMs \nusing the sklearn library, from scratch and with a classical and quantum kernel.\n* In the [kernels.py](kernels.py) file, you can find the implementation of the classical and quantum kernels.\n* In the [scratch.py](scratch.py) file, you can find the implementation of the SVM algorithm from scratch.\n* In the [visualization.py](visualization.py) file, you can find the implementation of the visualization of the\ndecision boundary of the SVMs.\n\n## Requirements\n* Python 3.8 or higher\n* Other requirements are in the [requirements.txt](requirements.txt) file. You can install them using the following \ncommand:\n```bash\npip install -r requirements.txt\n```\n\n\n## Note\nThis repository is currently under construction. More details will be added soon.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremiegince%2Flearning_svm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjeremiegince%2Flearning_svm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeremiegince%2Flearning_svm/lists"}